Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
VitaDAO is a community-driven organization funding and acquiring IP for longevity-related research, emphasizing open science and community governance.
Detailed case-study description drawing on VitaDAO's public documentation, governance records, and whitepaper materials.
Research agenda priorities include: empirically quantifying the value of digital twins on R&D productivity; studying complementarities between AI tools and tacit sensory knowledge; measuring cultural translation costs; and analyzing market concentration risks from proprietary sensory models.
List of recommended empirical research directions derived from conceptual analysis and gap identification; no primary empirical work conducted within the paper itself.
The collection highlights resolving methodological challenges such as ecological validity, generalization across environments, and integrating domain knowledge rather than purely optimizing benchmarks.
Methodological-focus summary from the collection indicating emphasis on ecological validity, generalization, and domain-knowledge integration across multiple papers.
Early applications focused on automating straightforward, repetitive tasks (e.g., filtering blank camera‑trap images); current work aims for deeper integration with ecological questions.
Historical-arc observation drawn from the collection's examples and classifications of papers (descriptive review of prior vs. current papers in the collection).
The AI–ecology interface is maturing from simple, task‑automation proofs of concept into genuinely interdisciplinary work that advances both AI methods and ecological science.
Synthesis of the paper collection (mix of methodological, empirical, and translational papers) and the paper's summary of trends across those contributions (no single-sample experiment; claim based on cross-paper review).
Seed 2.0 Lite achieved 75.7% success rate with-skill, an increase of +18.9 percentage points over baseline.
Model-specific reported result in the paper: Seed 2.0 Lite with-skill success rate (75.7%) and reported improvement (+18.9pp); reported from the benchmark runs.
GLM-5 Turbo achieved 78.4% success rate with-skill, an increase of +5.4 percentage points over baseline.
Model-specific reported result in the paper: GLM-5 Turbo with-skill success rate (78.4%) and reported improvement (+5.4pp); based on the benchmark evaluation.
Nemotron 120B achieved 78.4% success rate with-skill, an increase of +18.9 percentage points over baseline.
Model-specific reported result in the paper: Nemotron 120B with-skill success rate (78.4%) and reported improvement (+18.9pp); results drawn from the benchmark runs.
MiniMax M2.5 achieved 81.1% success rate with-skill, an increase of +13.5 percentage points over baseline.
Model-specific reported result in the paper: MiniMax M2.5 with-skill success rate (81.1%) and reported improvement (+13.5pp); based on subset of the 185 scenario-runs across the evaluated models.
Results across 5 open-weight model conditions and 185 scenario-runs show consistent skill lift across all models.
Aggregate experimental results reported in the paper: evaluation over 5 model conditions and 185 scenario-runs, with cross-model improvement when SKILL is provided.
Returns to advanced digital skills vary by firm size/type: the wage return in large Chaebol conglomerates is approximately 18.7%, significantly higher than the ~9.5% return in Small and Medium-sized Enterprises (SMEs), indicating a 'skills–scale' complementarity effect.
Heterogeneity analysis within the extended Mincerian wage regression framework using KLIPS micro-data, comparing estimated returns across firm types (Chaebol vs SMEs). (Sample size and exact model specification not provided in the excerpt.)
Workers with only general digital literacy receive a wage premium of approximately 5.8% (after controlling for education, experience, and demographics).
Same empirical framework: extended Mincerian wage equation on KLIPS micro-data with controls for education, experience, and demographic characteristics. (Sample size not specified in the provided excerpt.)
Workers possessing specialized digital skills (e.g., data analysis, programming, automation control) enjoy a significant wage premium of approximately 14.2% after controlling for years of education, work experience, and demographic characteristics.
Empirical estimation using an extended Mincerian wage equation on micro-data from the Korean Labor and Income Panel Study (KLIPS); models control for years of education, work experience, and demographic covariates. (Sample size not specified in the provided excerpt.)
AI-adopting firms increase R&D expenditures following adoption.
Firm financial data showing higher R&D spending for adopters relative to nonadopters in post-adoption periods using the diff-in-diff framework.
Post-adoption patents by AI adopters receive more citations than those of nonadopters.
Difference-in-differences estimates comparing citation counts per patent before and after AI installation versus nonadopters; patent citation data used as the dependent variable.
Firms that adopt AI subsequently increase patenting relative to nonadopters.
Firm-level analysis using a novel AI adoption measure based on timing of AI product installations and a stacked difference-in-differences design exploiting staggered adoption; dependent variable = firm patent counts (patenting rate). (Sample size and exact time period not specified in the provided text.)
Programming experience significantly improved code security.
Association found in the study between participants' programming experience (general programming experience measured for each participant) and the security of their submitted code; statistical analysis in the sample (n = 159) showed a significant positive effect of experience on code security.
Using distributed systems as a principled foundation is a useful approach for creating and evaluating LLM teams.
Primary methodological proposal of the paper; supported by conceptual argument and (per the paper) mappings between distributed-systems concepts and LLM team design (specific experimental validation not detailed in the excerpt).
Large language models (LLMs) are growing increasingly capable.
Statement in the paper's introduction/abstract summarizing the field; based on observed progress in LLM development cited by the authors (no experimental sample size provided in the excerpt).
Only seven specialized skills produce meaningful gains (up to +30%).
Empirical results showing that 7 out of 49 skills yielded meaningful positive improvements in acceptance-test pass rates, with gains up to 30%.
The average gain from injecting skills is only +1.2% in pass rate.
Aggregated pass-rate differences computed across the benchmark tasks comparing with-skill vs without-skill conditions, reported as an average +1.2% gain.
Analysis of benchmark data (n = 667) reveals substantial synergy effects: Llama-3.1-8B improves human performance by 23 percentage points.
Empirical analysis of the same benchmark dataset (n = 667) using the Bayesian IRT model; reported improvement in human performance with Llama-3.1-8B assistance of +23 percentage points.
Analysis of benchmark data (n = 667) reveals substantial synergy effects: GPT-4o improves human performance by 29 percentage points.
Empirical analysis of a benchmark dataset of n = 667 using the paper's Bayesian IRT framework; reported improvement in human performance with GPT-4o assistance of +29 percentage points.
The work offers a blueprint for converting the ideological potential of AI into implementable, regulator-compatible utilities in pharmaceutical science by synthesizing quantitative measures and practical measures.
Claim about the paper's contribution (blueprint). It is an author claim about the synthesis and guidance provided; the excerpt does not include empirical validation that following the blueprint yields successful implementation.
The paper proposes a systematized framework of integration that emphasizes creating high-impact pilot projects, in-the-wild testing, and ongoing monitoring of models in accordance with FDA, EMA, and EU AI Act guidance.
Described as the paper's proposed framework and recommendations for regulatory-aligned implementation. The excerpt indicates the proposal but does not present validation or empirical testing of the framework.
Grounded in the Resource-Based View (RBV), AI is conceptualized as a strategic intangible resource that can confer a competitive advantage when integrated with complementary capabilities.
Theoretical framing presented in the paper (RBV-based conceptualization); not an empirical finding but an explicit conceptual claim.
Firms with high AI adoption had an average profit growth rate of 9.5%, compared to 5.8% for low adopters.
Reported profit growth rates for high vs. low AI adoption groups from the questionnaire data (N=400); the paper gives the specific averages: 9.5% (high adopters) vs. 5.8% (low adopters).
O artigo discute implicações gerenciais e de políticas públicas para reduzir fricção, acelerar adoção responsável e orientar investimentos em produtividade e inclusão.
Seção de discussão mencionada no resumo abordando encargos gerenciais e políticas públicas; não há avaliação empírica de políticas no resumo.
O artigo entrega instrumentos replicáveis — a escala SCF-30, um checklist de governança mínima de IA e uma matriz 30-60-90 dias — para uso prático.
Afirmação explícita no resumo de que instrumentos replicáveis são disponibilizados; presunção de inclusão dos instrumentos no corpo do artigo.
AI significantly enhances firms' total factor productivity (TFP).
Empirical results from the multidimensional fixed-effects panel model applied to the 2007–2023 sample of agricultural A-share firms; statistical significance reported in the paper.
The model is disciplined using data from the Michigan Survey of Consumers and the Survey of Professional Forecasters, targeting key empirical moments.
Calibration/estimation strategy described in the paper: parameters are chosen to match moments from the Michigan Survey of Consumers and SPF (targeted empirical moments). Specific moments and calibration targets are reported in the paper.
I develop a search-and-matching model with sticky wages and endogenous separations.
Theoretical/model contribution: construction and analysis of a calibrated search-and-matching framework that incorporates wage stickiness and endogenous separation decisions.
Workers and firms face information frictions about the aggregate state of the economy (modeled explicitly).
Assumption and mechanism built into the paper's theoretical framework: a search-and-matching model with information frictions for both sides of the market (model specification).
Households form dispersed, backward-looking expectations about macroeconomic conditions.
Survey evidence from the Michigan Survey of Consumers showing dispersion in individual expectations and patterns consistent with backward-looking (slow/updating) belief formation about macro variables; exact sample sizes and empirical specifications are provided in the paper (not in the summary).
High-quality chatbots (96–100% accurate) improved caseworker accuracy by 27 percentage points.
Experimental result reported in paper: treatment with chatbots at 96–100% aggregate accuracy produced a 27 percentage-point increase in caseworker accuracy compared to control; based on the randomized experiment on the 770-question benchmark.
Caseworker performance significantly improves as chatbot quality improves.
Aggregated results from the randomized experiment show monotonic improvement in caseworker accuracy as the chatbot suggestion accuracy increases; paper states the improvement is statistically significant (specific p-values/statistical tests not provided in the excerpt).
AI-integrated fuel blending systems achieve very high precision, demonstrated by a coefficient of determination (R2) of 0.99 during validation.
Model validation results reported in the paper (fuel blending system validation, R2 = 0.99), indicating very high explanatory/ predictive fit compared to traditional models.
DARE posits that responsible AI deployment requires the simultaneous and integrated development of Digital readiness, Administrative governance, Resilience & ethics, and Economic equity.
Descriptive claim about the framework's components as reported in the abstract (conceptual proposition).
This paper introduces the DARE Framework, a holistic, four-dimensional model for national AI strategy and international cooperation.
Factual description of paper content in abstract — the framework is introduced by the authors (conceptual/model contribution).
ERM is an integrated, strategic framework that aligns risk management with corporate governance, objective setting, and performance management.
Conceptual descriptions and definitions of ERM drawn from existing ERM frameworks and literature reviewed in the article.
The authors curated a set of guidelines called the Incentive-Tuning Framework to aid researchers in designing effective incentive schemes for human–AI decision-making studies.
Authors' contribution described in the paper: development of a framework (framework content and evaluation details not provided in excerpt).
The intelligent scheduling model incorporates legal, contractual, skill-based, and preference-aware constraints to generate equitable and efficient rosters.
Methodological description of constraints encoded in the optimization model for scheduling; experimental validation of resulting rosters reported (conflict reduction and fairness metrics), but specific constraint formulations and datasets are not detailed in the excerpt.
The performance evaluation framework combines structured metrics (task completion, attendance, punctuality) with unstructured feedback (patient surveys, peer reviews) analyzed using natural language processing.
Methodological description in the paper of the performance evaluation module and use of NLP for unstructured feedback analysis; implementation details and dataset sizes not specified in the excerpt.
The proposed AI-driven HRM framework integrates forecasting, optimization, and performance evaluation to enhance workforce planning, staff scheduling, and continuous assessment.
Methodological contribution described in the paper: framework design with three core modules (demand forecasting, intelligent scheduling, performance evaluation); validated via experiments on synthetic and real hospital datasets (dataset sizes not specified in the text).
The Indian government believes that artificial intelligence (AI) will play an important role in India’s continued economic growth, both through its contribution to productivity in the private sector and through smarter and more data-led government.
Reported position in the paper based on review of government statements and policy documents (policy analysis/legal review). No empirical sample size applies; claim is descriptive of government belief.
The positive impact of generative AI on ESG performance is stronger in manufacturing firms, firms in eastern regions, and technology-intensive firms (relative to non-manufacturing, central/western regions, and non-technology-intensive firms).
Heterogeneity/subsample analysis on the panel of Chinese A-share firms (2012–2024) comparing effects across firm types, geographic regions, and technology intensity, showing larger estimated positive effects for manufacturing, eastern-region, and technology-intensive subsamples.
Sustainable innovation partially mediates the relationship between generative AI and corporate ESG performance improvement.
Mediation analysis conducted on the panel dataset (Chinese A-share firms, 2012–2024) indicating a partial mediating role for sustainable innovation measures between generative AI use and ESG performance.
The quality of information disclosure partially mediates the relationship between generative AI and corporate ESG performance improvement.
Mediation analysis (intermediary variable tests) performed on the same panel (Chinese A-share firms, 2012–2024) showing that information-disclosure quality accounts for part of the effect of generative AI on ESG outcomes.
Generative AI can effectively drive improvements in corporate ESG performance.
Empirical analysis using panel data of Chinese A-share listed firms covering 2012–2024; the paper reports an econometric panel-data model showing a positive effect of generative AI adoption/use on measured firm ESG performance.
The study extends human capital theory by integrating emotional and psychological dimensions into explanations of productivity and employment outcomes.
Theoretical contribution asserted by the authors based on their empirical findings linking emotional intelligence and psychological factors to economic outcomes; this is a conceptual extension rather than a statistical result.