Evidence Explorer (34 categories)

One row per outcome category — the bird's-eye view. Use this to compare topics against each other: which have the most agreement, the strongest evidence, the most recent activity. To read the individual claims behind a category instead, use the Evidence page (or click any row here to drop into them).

Pick a lens to re-sort the board; click any column header to sort by it; click a row to browse its claims.

What do these lenses and columns mean?

Lenses

Consensus: How much the papers in a category agree on the direction of the effect.
Strength: How strong the underlying evidence is, by study design and grade.
Momentum: How much new research is arriving in the category.
Contested: How much the findings conflict with each other.
Coverage: How much evidence exists in the category at all.

Columns

Papers / Claims: Distinct papers, and total extracted claims, in the category.
Pos / Neg: Papers whose claims lean positive or negative once grouped by paper.
Direction: The balance bar. The badge reads "Positive" or "Negative" when at least 60% of directional papers agree, otherwise "Split".
Depth: A Solid / Moderate / Thin roll-up of evidence strength. Hover a cell for the exact formula.
Causal %: Share of papers using an RCT or quasi-experimental design.
High-grade %: Share of claims the model graded "high".
Gen.: Distinct units of analysis the evidence spans. A rough breadth proxy, not a formal external-validity test.
Effect: The most common effect-size phrase in the category. Only about 11% of claims carry one.
30d / 90d: Papers added in the last 30 and 90 days. The bar is scaled to the busiest category.
Tensions: Pairs of claims that point opposite ways (one positive, one negative) across different papers. Machine-detected candidates, not confirmed contradictions: the rule does not check whether the two claims describe the same population, period, or outcome.
Synthesis: A written "state of the evidence" page, where one has been published.

Every label here is AI-generated and unverified. See the evidence layer documentation for how it is built and where it falls short.

Primary: Secondary:

Category	Papers	Pos	Neg	Direction	Depth	Causal %	High-grade %	Gen.	Effect	30d	90d	Tensions	Claims	Synthesis
Organizational Efficiency 📄	1886	1246	320	Positive	Moderate	14.1%	5.8%	31	β = 0.49, p < 0.001	435	1348	⚡ 1213618	4657	View
Governance & Regulation 📄	1723	986	377	Positive	Moderate	10.0%	6.4%	47	Not quantified	270	1029	⚡ 1048276	4155	View
Technology Adoption Rate 📄	1322	775	284	Positive	Moderate	13.8%	8.2%	37	over 80%	182	754	⚡ 433043	2743	View
Decision Quality	1024	595	237	Positive	Solid	17.9%	14.4%	18	≈20%	235	734	⚡ 426313	2681	No synthesis yet
Output Quality 📄	865	502	203	Positive	Solid	22.9%	12.5%	22	Ω(sqrt(M/D))	150	548	⚡ 372089	2369	View
AI Safety & Ethics	901	264	462	Negative	Moderate	10.5%	10.5%	17	k agents	224	627	⚡ 319229	2022	No synthesis yet
Research Productivity 📄	668	320	93	Positive	Moderate	9.3%	14.7%	19	18-25% increase	53	282	⚡ 97739	1714	View
Firm Productivity 📄	739	524	85	Positive	Moderate	13.7%	4.0%	8	β1 = 0.714 (p-value = 0.000, which is less than α = 0.01)	140	453	⚡ 121575	1637	View
Task Allocation 📄	892	412	116	Positive	Solid	15.6%	10.0%	23	79%	226	669	⚡ 95576	1586	View
Market Structure	629	202	252	Split	Moderate	7.2%	2.9%	18	≈ USD 220bn; USD 270–500bn	122	371	⚡ 106183	1290	No synthesis yet
Innovation Output	489	357	58	Positive	Solid	18.2%	3.9%	17	partial mediation	83	307	⚡ 46677	999	No synthesis yet
Task Completion Time	501	340	95	Positive	Solid	24.4%	9.6%	14	from hours to minutes	92	329	⚡ 44362	767	No synthesis yet
Firm Revenue	374	254	70	Positive	Solid	17.1%	8.2%	6	linear in own liquidity	45	220	⚡ 27682	705	No synthesis yet
Consumer Welfare	335	160	95	Positive	Solid	17.0%	9.2%	12	approximately 28 to 40 percent of the difference between recommendations	70	208	⚡ 32924	695	No synthesis yet
Skill Acquisition 📄	452	263	79	Positive	Moderate	11.7%	3.2%	15	g = 0.14, 95% CI: [-0.18, 0.47]	77	255	⚡ 20551	685	View
Error Rate	395	151	199	Split	Solid	21.3%	13.0%	13	73% reduction	102	280	⚡ 45012	677	No synthesis yet
Employment Level 📄	341	108	80	Split	Moderate	13.8%	5.2%	14	≈80% employment rate (prime-age 25–54)	64	186	⚡ 21657	675	View
Inequality Measures 📄	374	52	242	Negative	Moderate	11.5%	4.2%	13	50–70%	76	221	⚡ 20049	621	View
Fiscal & Macroeconomic	239	115	76	Positive	Moderate	10.9%	1.7%	10	−0.041, p < 0.001	35	131	⚡ 20663	586	No synthesis yet
Worker Satisfaction	291	133	103	Split	Solid	25.1%	4.9%	6	ρ ∈ ((5√73 − 17)/48, 1)	51	188	⚡ 24971	551	No synthesis yet
Automation Exposure 📄	299	99	116	Split	Moderate	10.7%	4.8%	13	χ² = 43.27, p < 0.01	80	196	⚡ 14107	477	View
Regulatory Compliance	224	106	85	Split	Moderate	13.4%	6.2%	9	β = 0.487, p < 0.01	55	124	⚡ 13362	421	No synthesis yet
Team Performance	180	112	32	Positive	Solid	25.0%	7.8%	9	≈0.8322 (recovers decentralized benchmark)	28	110	⚡ 6690	347	No synthesis yet
Developer Productivity	187	126	32	Positive	Solid	19.8%	7.5%	10	2.09x the pre-mandate baseline	24	104	⚡ 7942	346	No synthesis yet
Wages & Compensation 📄	196	80	73	Split	Solid	21.4%	7.5%	9	β = 0.61, p < 0.001	33	112	⚡ 9185	332	View
Training Effectiveness	223	160	32	Positive	Moderate	13.9%	4.8%	11	Õ(ε^-4) total samples	48	126	⚡ 4427	332	No synthesis yet
Job Displacement	216	20	136	Negative	Moderate	6.0%	1.9%	9	µ = 3.34/5	42	119	⚡ 1811	260	No synthesis yet
Hiring & Recruitment	122	60	38	Positive	Solid	18.9%	9.3%	7	increase of 300 job openings	23	81	⚡ 4811	225	No synthesis yet
Creative Output	66	34	17	Positive	Solid	28.8%	7.7%	6	5.5 to 10.2 percentage point higher likelihood	11	38	⚡ 2014	169	No synthesis yet
Skill Obsolescence	120	5	88	Negative	Moderate	6.7%	1.8%	8	39%	20	66	⚡ 821	163	No synthesis yet
Labor Share of Income	67	15	34	Negative	Moderate	11.9%	4.6%	5	πK → 1; πH → 0; πAI → 0	11	35	⚡ 941	109	No synthesis yet
Social Protection	70	41	20	Positive	Moderate	14.3%	2.9%	7	public cultural services can act as productive social infrastructure advancing SDG 8 (decent work) given adequate digital capacity	7	30	⚡ 698	104	No synthesis yet
Worker Turnover	54	26	23	Split	Moderate	13.0%	2.3%	6	β = −0.29, 95% CI [−0.41, −0.17]	10	34	⚡ 567	87	No synthesis yet
Industry	1	0	0	—	Moderate	0.0%	0.0%	1	—	—	—	—	1	No synthesis yet

⚡ A tension count is how many claim pairs in a category point opposite ways (one positive, one negative) across different papers. These are machine-detected candidates, not confirmed contradictions: the rule does not check whether the two claims describe the same population, period, or outcome. Read both papers before treating any pair as a real disagreement.