Evidence (6869 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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Blindfolding (anonymizing identifiers) allows verification of whether meaningful predictive signals persist (i.e., predictions reflect legitimate patterns rather than pre-trained recall of tickers).
Combined methodological-and-result claim: approach described (anonymization) plus stated objective and reported validation (negative controls and reported Sharpe under anonymization). Specific experimental protocol and quantitative results isolating the effect of anonymization are not provided in the excerpt.
On 2025 year-to-date (through 2025-08-01), the system achieved Sharpe 1.40 +/- 0.22 across 20 random seeds.
Backtest/performance claim: reported Sharpe ratio with reported uncertainty and a sample size of 20 seeds; time window specified as 2025 YTD through 2025-08-01. No further details on portfolio construction, leverage, transaction costs, or benchmark adjustment provided in the excerpt.
Regulatory sandboxes offer a flexible and innovation-friendly governance model compared to traditional command-and-control mechanisms.
Normative and comparative analysis within a law & economics framework; no empirical performance data reported in the abstract.
Comparative insights from FinTech identify the institutional design features necessary to ensure the effectiveness and resilience of regulatory sandboxes.
Comparative case-based reasoning drawing on FinTech regulatory sandbox experience (abstract does not report number or selection of cases).
AI regulatory sandboxes may correct specific government failures, including regulatory capture, rent-seeking, and knowledge gaps.
Analytical claims supported by comparative reasoning (FinTech examples) and economic analysis of government failure; no empirical testing or sample size reported in the abstract.
AI regulatory sandboxes facilitate iterative regulatory learning while promoting responsible AI innovation.
Theoretical argument using experimentalist governance concepts and law & economics reasoning; comparative insights referenced but no empirical sample detailed in the abstract.
AI regulatory sandboxes can reduce negative externalities associated with AI deployment.
Conceptual and economic analysis in the paper (no empirical quantification or sample size reported in the abstract).
AI regulatory sandboxes can mitigate information asymmetries between regulators and firms.
Analytical application of an economic analysis of law framework; theoretical argumentation rather than reported empirical measurement in the abstract.
A well-established legal framework for data privacy (e.g., PIPL) enhances the benefits of big data for corporate performance.
Inference drawn from the observed stronger positive big-data effect on firm value after PIPL implementation, as reported by the paper's moderation analysis.
Robust sensitivity tests confirm the main findings, indicating that the results are not driven by model specification or sample selection.
Paper reports multiple robustness/sensitivity checks (unspecified in summary) that the authors state produce consistent results supporting the primary conclusions.
The positive impact of big data on firm performance is strengthened following the implementation of China's Personal Information Protection Law (PIPL).
Moderation/interacted-specification analysis in the paper comparing pre- and post-PIPL periods (or interacting big-data measure with a PIPL indicator), showing a larger positive effect on firm value after PIPL implementation.
The positive effect of big data on firm value operates through improving operational efficiency and reducing costs.
Mechanism analysis reported in the paper indicating mediation/channel tests where big data adoption is associated with measures of operational efficiency and cost reductions, which in turn relate to higher firm value.
Big data application significantly improves firm value.
Results from fixed-effects regressions on the 2007–2021 panel showing a statistically significant positive coefficient for the big-data keyword-frequency measure on firm value (paper reports significance and effect direction).
It is optimal to start taxing AI when cognitive workers start to consider switching to manual jobs.
Analytical result derived from the extended dynamic taxation model and its comparative-static/optimal-policy analysis; the timing rule for introducing an AI tax follows from the model's equilibrium conditions and welfare optimization.
The model implies testable governance diagnostics linking latent fragility to observable patterns: recorded dissent (anonymous vs. formal voting gaps), scenario-set diversity, pipeline and method concentration, and anchor lag.
Theoretical mapping from model primitives and observable quantities to proposed diagnostics; the paper enumerates observable patterns that should correlate with model-implied fragility. This is a theoretical implication rather than an empirically validated claim.
Pidgin significantly outperformed standard English on measures of knowledge transfer across agriculture, education, and health domains.
Aggregate analysis of questionnaire comprehension items (44-item instrument) across domain-specific modules administered to 45 participants; comparative language-performance results reported in study.
Volunteers who used proverbs and vernacular registers were incorporated into local kinship structures, granted traditional titles, and perceived as legitimate development actors rather than outsiders.
Qualitative evidence from participant observation and discourse samples collected during fieldwork; interview and questionnaire items on perceptions of volunteer legitimacy and social integration.
Agricultural techniques taught in Pidgin were nearly universally adopted by recipients.
Self-reported adoption/behavior-change items in the 44-item questionnaire and corroborating qualitative observation of agricultural practice among participants in the sample (N = 45).
Pidgin-mediated interventions achieved large comprehension gains on health messaging, exceeding 30 percentage points compared with standard English.
Quantitative comparison derived from the 44-item field questionnaire (comprehension items) administered to the 45-participant sample; reported percentage-point difference (>30 pp) in health-message comprehension by language of instruction.
Using Cameroon Pidgin English as the primary medium for Peace Corps development work produced substantially better knowledge transfer, uptake, and social legitimacy than standard English.
Mixed-methods field study of Peace Corps interventions in Cameroon's Northwest: 44-item questionnaire administered to 45 participants across agriculture, education, and health; quantitative measures of comprehension and self-reported adoption; supplemented by qualitative observation and discourse samples.
AI adoption will shift fact-checking tasks (more monitoring, less rote verification), creating a need for reskilling and new roles (AI tool operators, analysts); donor and public investments should fund capacity building for local organizations.
Workforce implications inferred from interview reports about changing task mixes and the study's interpretive recommendations.
Investments should prioritize hybrid models where automation provides scale and humans handle contextual, adversarial, and legally sensitive judgments.
Recommendation based on interview findings about AI benefits and limitations and the study's interpretive synthesis.
The study distills context-sensitive best practices for fact-checking in restrictive environments, including safety protocols, local partnerships, and hybrid verification workflows.
Synthesis of findings from document analysis and interviews producing a set of recommended practices documented in the study's outputs.
AI can lower verification costs and scale reach by automating tasks such as classification, clustering, alerting, and translation.
Interview reports from platform staff and interpretive analysis identifying AI-assisted use cases for prioritization, monitoring, and translation.
Community reporting and audience-focused formats are used to improve engagement.
Platform outputs and staff interviews describing deployment of community-reporting mechanisms and tailored audience formats.
Platforms form partnerships with media outlets, academic institutions, and civil-society actors to amplify reach and secure data.
Interview accounts and organizational documents describing cross-sector partnerships and collaboration arrangements.
Transparent workflows and clear labeling are used to build credibility with audiences.
Document analysis of platform outputs and guidelines showing explicit workflow transparency and labeling practices, supported by interview statements.
Platforms emphasize local-language expertise and culturally grounded sourcing as a strategy to improve verification and credibility.
Observed practices and platform guidelines derived from document analysis and staff interviews describing the use of local-language expertise and sourcing.
Practical policy recommendation: require transparent documentation and third‑party auditing for high‑impact LLM deployments and subsidize public‑interest evaluation infrastructure.
Policy prescription supported by the paper's normative and economic analysis; no pilot implementation or empirical evaluation of the recommendation is provided.
Policy levers that can address alignment externalities include disclosure requirements (data provenance, evaluation practices), mandatory participatory evaluation for high‑impact systems, standards for auditing, procurement rules favoring participatory transparency, and liability/certification regimes.
Policy recommendation based on economic and governance reasoning and synthesis of prior regulatory proposals; no policy pilot data or impact evaluation is reported.
Economics research should develop multi‑dimensional metrics capturing welfare, distributional impacts, and autonomy rather than relying on single aggregate accuracy or safety scores.
Prescriptive recommendation grounded in critique of current benchmarking practices and theoretical desiderata; no new metric is empirically validated in the paper.
Dynamic constraints (continuous monitoring, feedback loops, and configurable safety settings that adapt post‑deployment) are preferable to static pre‑deployment-only safety fixes.
Conceptual argument and synthesis of deployment experience and monitoring literature; suggestions for operational tooling and monitoring rather than empirical evaluation.
Participatory governance—includes varied stakeholders such as users, affected communities, domain experts, and regulators in design, evaluation, and deployment decisions—will improve alignment outcomes and legitimacy.
Theoretical and normative argument citing participatory design literature and ethical governance scholarship; paper offers procedural recommendations but no empirical trial of governance models.
Alignment should shift from static, post‑training constraints (one‑off fixes like safety filters or RLHF alone) to dynamic, participatory systems that explicitly protect pluralism, autonomy, and justice.
Normative argument and conceptual synthesis drawing on literature in AI safety, value alignment, and participatory design; prescriptive reasoning rather than original empirical results.
Investment choices in collaboration AI and digital infrastructure become central strategic decisions affecting firms' comparative advantage.
Management literature synthesis and illustrative multinational cases; argument is conceptual without firm‑level comparative empirical data presented in the paper.
AI collaboration tools (virtual assistants, meeting summarizers, asynchronous platforms) complement hybrid work by reducing coordination costs and supporting dispersed teamwork.
Conceptual integration of technology and organizational literature; supported by illustrative case examples of multinational organizations but not by new quantitative causal evidence.
Hybrid and remote work increase employee autonomy and work–life integration.
Conceptual synthesis of sociological and management literatures; supported by secondary data and illustrative case studies from multinational organizations. No primary quantitative analysis or sample size reported—based on comparative case illustrations and theoretical integration.
Tariff reductions and expanded supply channels following CAFTA contributed as secondary channels to increased third‑country agricultural imports.
Paper documents tariff changes and supply‑channel expansion as part of mechanism analysis; DID and mediator tests link tariff reductions and expanded channels to import outcomes.
CAFTA improved logistics and service frictions (e.g., storage, logistics performance) relevant to agricultural imports.
Secondary channel analysis using logistics/storage indicators and related service frictions available in the data; assessed as mediators in the DID framework.
CAFTA widened China's trading‑partner and product diversity in agricultural imports, increasing both partner and product variety from third countries.
DID estimates on partner and product diversity metrics constructed from customs import records (2000–2014); reported changes in diversity as outcomes in the paper.
A complementary‑products linkage effect is a key mechanism: expanded channels and product complementarities make sourcing non‑ASEAN goods easier and more attractive.
Mechanism analysis using product‑level and partner‑level import data (China Customs) showing increased imports of complementary products and linkages consistent with this channel in DID estimates.
The primary spillover mechanism is a 'low‑cost import experience' effect: cheaper/consistent regional sourcing lowers firms' marginal cost of engaging additional foreign suppliers, encouraging imports from third countries.
Mechanism tests using mediator variables (cost/procurement indicators) within the DID framework and firm‑level data; reported as the main channel in the paper's analysis.
A new market will emerge for controls, certification, attestations, secure toolchains, and audited model deployments; compliance costs will shape comparative advantages among firms and countries.
Policy-market synthesis and analogies to certification markets in other regulated tech domains (qualitative).
The emergence of HACCAs will create a demand shock for defensive cyber tools and services (AI-based detection, incident response, resilience engineering), accelerating R&D and capital allocation into defensive AI.
Market-impact scenario analysis and industry inference about defensive responses to heightened threats (qualitative forecasting).
Generative AI functions as a socio‑technical intermediary that facilitates interpretation, coordination, and decision support rather than merely automating discrete tasks.
Thematic analysis and co‑word linkage between terms related to interpretative work, coordination, and decision‑support and technical GenAI terms within the corpus.
The literature indicates a managerial shift away from hierarchical command‑and‑control toward guide‑and‑collaborate paradigms, where managers curate, guide, and coordinate AI‑augmented teams rather than micro‑manage tasks.
Synthesis of themes from the 212‑paper corpus (co‑word and thematic analyses) showing recurrent managerial/behavioural concepts such as autonomy, coordination, and decision‑support tied to GenAI discussions.
Higher educational attainment is positively associated with greater willingness to keep working before retirement.
Multivariate regression analysis of the cross-sectional survey (n=889) using education level as a key explanatory variable.
Male gender is positively associated with higher willingness to remain employed before retirement.
Multivariate regression on the survey sample (n=889) including gender as an explanatory variable, controlling for demographic and socioeconomic covariates.
Standardized data schemas and interoperable protocols reduce transaction costs and increase returns on AI investments; public-good components (shared taxonomies, open benchmarks) will accelerate innovation in DPP ecosystems.
Policy/economic recommendation synthesized from empirical observations about interoperability needs (survey and qualitative inputs) and economic reasoning; not directly measured as an outcome in the study.
Different consumer segments imply different AI-driven engagement strategies: targeted personalization and recommender systems for 'aware' consumers, and default, nudging, and tangible-benefit signals for 'unaware' consumers.
Derived from k‑means segmentation results and implication discussion linking consumer cluster characteristics to appropriate AI/UX interventions; segmentation is empirical, the AI-prescription is inferential.