Evidence (2432 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Labor Markets
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Firm learning raises the persistence of the economy's response to shocks but dampens volatility.
Quantitative model experiments: introducing firm learning into the calibrated model increases impulse-response persistence to shocks (higher persistence) while reducing the magnitude/variance of fluctuations (lower volatility) in simulated aggregate variables.
The rapid global proliferation of Artificial Intelligence (AI) has created a profound paradox: while promising unprecedented productivity gains, its current trajectory exacerbates labor market polarization, deepens inequality, and threatens to fracture the 20th-century social contract.
Asserted in abstract; no empirical methods, datasets, or sample sizes described in the abstract (presumably supported in paper by literature review/argumentation).
AI’s labor market impacts in the Philippines are not technologically predetermined; outcomes will depend on policy choices related to skills development, governance, social protection, and innovation.
Integrated conceptual framework in the paper linking AI capabilities, occupational structure, and institutional mediation, supported by the scenario analysis which shows divergent outcomes conditional on policy settings.
Observed AI adoption patterns in the Philippines to date are cautious, with limited job loss but growing task reconfiguration and emerging skills gaps.
Firm- and worker-level evidence on AI adoption (surveys/interviews and/or administrative firm adoption data described in the paper) documenting current adoption practices, reported job impacts, task changes, and reported skill shortages.
A significant share of Philippine employment is exposed to generative AI—particularly in service-sector and BPO-related occupations.
Occupational exposure analysis using Philippine labor force data (occupational employment shares and task-content measures) combined with task-level evidence on generative AI capabilities.
AI alters job structures, workflow patterns, and human roles in decision-making processes.
Thematic content analysis of recent accredited journal literature as part of the qualitative library research (sources not enumerated).
AI is fundamentally transforming the workplace by creating new opportunities, intensifying challenges, and redefining professional skills.
Qualitative library research: systematic documentation and thematic content analysis of recent accredited journal sources (number of sources not specified).
The actions of large employers in an occupation or industry affect local and national wages, employment and output.
Theoretical/empirical claim in the paper; excerpt does not supply empirical methods, identification, or sample sizes demonstrating these effects.
As AI becomes increasingly integrated into higher education, instructors and institutions face urgent questions about its implications for teaching, learning, scholarly practice, and for power, agency, and access.
Framing claim in the paper's introduction supported by literature context and reinforced by the study's analysis of practitioner (faculty) discussions on Reddit indicating concern/uncertainty. (The excerpt does not report survey or quantitative prevalence data on how widespread these concerns are.)
Through thematic content analysis, the study explores faculty perceptions, pedagogical tensions, and imaginative possibilities surrounding AI’s academic role.
Method stated by author: thematic content analysis of subreddit discussions to identify themes relating to faculty perceptions, pedagogical tensions, and imagined futures for AI in academia. (Exact number of themes, coding procedure, and sample size not provided in excerpt.)
AI reshapes traditional power structures, challenges regulatory frameworks, and redefines global governance mechanisms.
Broad analytic claim supported by comparative policy analysis and qualitative document review; the paper frames this as an overarching conclusion without reporting quantitative indicators or case counts.
The geopolitics of AI constitutes not only a competition for technological supremacy but also a contest over the moral and institutional foundations of global governance.
Theoretical synthesis drawing on international relations theories (realism, liberal institutionalism, constructivism) and comparative policy analysis; presented as an interpretive conclusion rather than empirically quantified.
AI represents a new dimension of geopolitical power that influences how states project authority, regulate innovation, and negotiate global norms.
Argument based on comparative policy analysis and qualitative document review of state and multilateral policy documents (specific documents and number not enumerated in text).
Artificial intelligence (AI) has emerged as one of the most transformative forces shaping the 21st-century international order.
Conceptual claim supported by literature review and theoretical framing in the paper (no empirical sample or quantitative data reported).
These findings underscore the importance of timing when evaluating demographic policy: stabilizing finances within a practical timeframe requires levers that improve the budget directly, rather than those that work through slow demographic channels.
Comparative timing analysis from multiple model scenarios showing faster fiscal improvement from direct budgetary levers (productivity, per-capita cost control) versus slow demographic interventions (fertility increases).
AI innovation effects on employment are cumulative and stage-specific over time.
Extended temporal analysis of cumulative and stage-specific impacts using the 268-city panel (2010–2023).
Knowledge democratization through AI may reduce educational inequality but may also exacerbate digital divides and erode universities' social mobility function.
Theoretical and socio-political analysis considering opposing effects; framed as a conditional/mixed outcome without empirical measurement reported in the paper.
AI displacement potential varies substantially across university functions.
Summary finding from the paper's comparative analysis of university functions; the paper provides ranked/percent estimates but does not report empirical sampling or statistical testing.
The Photo Big 5 provides predictive power comparable to race, attractiveness, and educational background.
Comparative predictive-performance analyses reported in the paper that evaluate Photo Big 5 against observables such as race, measured attractiveness, and education background within the same sample.
In a 2021 national labor survey, no single task was automated by more than 57% of respondents, compared with a maximum of 52% in the mid-2000s.
National labor survey results (mid-2000s vs 2021) as reported in the paper; survey details and sample size are not included in the excerpt.
Selection of human-LLM interaction archetype can influence LLM outputs and decisions.
Findings from the evaluation across clinical diagnostic cases (empirical comparison of archetypes' effects on outputs and decisions). Specific experimental details and sample size are not provided in the abstract.
We evaluate these diverse archetypes across real-world clinical diagnostic cases to examine the potential effects of adopting distinct human-LLM archetypes on LLM outputs and decision outcomes.
Empirical evaluation described in the paper using real-world clinical diagnostic cases. Method: application of archetypes to clinical cases and comparison of resulting LLM outputs and decisions. Sample size and specific case details are not provided in the abstract.
Generative artificial intelligence (GenAI) adoption is diffusing rapidly but its adoption is strikingly unequal.
Nationally representative UK survey data collected in 2023–2024 reporting adoption rates by subgroup; descriptive analysis of diffusion and disparities by demographic groups.
There is little existing knowledge about how the public perceives AI’s labor market impact and how those perceptions affect democratic attitudes and behaviors.
Literature gap claim motivating the study (based on authors' review of prior research; not empirically tested here).
Experts remain divided on whether AI will primarily displace human labor or generate new employment opportunities.
Statement based on prior literature and expert commentary cited in the paper (no new empirical test in this study).
Within the context of Nigeria, the adoption of advanced digital and AI-driven logistics solutions presents both a critical opportunity and a complex challenge for the country's seaports.
Analysis of secondary data sources focusing on Nigeria: academic literature by Nigerian scholars, Nigerian Ports Authority (NPA) performance reports, and policy documents as synthesized in the study.
AI is transforming jobs that are technical in nature.
Asserted in the paper's conceptual discussion of dual impacts; presented without empirical measurement or reported sample data in this paper.
Approximately 35% of gig workers use platforms as primary income sources and have limited alternative opportunities.
Classification of worker role and opportunity measures from labor force surveys and administrative records across the 24 OECD countries; proportion of gig workers identified as relying primarily on platform income.
Data maturity, ethical governance of algorithms, and industry type shape business performance in AI-augmented workflows.
Moderator/subgroup analyses and qualitative synthesis across the reviewed studies indicating these contextual factors influence outcomes; based on the 85-publication review.
Most moderators tested in the analyses have a considerable influence on the relationship between AI use and business performance.
Moderator analyses reported in the meta-analysis (unspecified number of moderators) across the sample of reviewed studies (n=85).
Digital transformation reshapes labor markets.
Paper asserts effects on labor markets (skills demand, employment patterns); the abstract lacks details on labor market data, sample sizes, or econometric analyses used to substantiate this claim.
AI, blockchain, and big data analytics affect productivity, investment strategies, labor markets, and regulatory frameworks.
Stated in the paper as impacts analyzed; the abstract does not specify the data, methods, or scope used to measure these impacts.
Digital transformation through artificial intelligence (AI), blockchain technology (BT), and big data (BD) analytics reconfigures economic mechanisms at both micro- and macroeconomic levels.
Paper-level analytic claim referencing impacts of AI, blockchain, and big data; detailed empirical methodology and sample information not described in the abstract.
A consistent finding is that implementation outcomes are determined by institutional conditions rather than algorithmic performance.
Synthesis across the 81 reviewed sources indicating recurring patterns where institutional factors (governance, reimbursement, workforce, regulations) drive implementation success more than raw algorithmic accuracy. Specific studies supporting this pattern are not named in the abstract.
The increasing integration of artificial intelligence (AI) into organizational decision-making has fundamentally reshaped how managers analyze information, evaluate alternatives, and exercise judgment.
Synthesis of interdisciplinary literature presented in this conceptual meta-analysis; no primary empirical sample or quantitative effect sizes reported in the abstract (literature review basis).
In digital tourism, there is both substitution potential (virtual experiences, demand management) and rebound risks that may offset emissions reductions.
Sectoral case synthesized from peer-reviewed studies and reports on digital tourism and travel demand (review-level evidence; no single empirical sample size).
Sustainable infrastructure and energy-transition analyses must account for hydrogen value chains and the substantial energy footprint of digital systems (data centers and AI workloads).
Review of sectoral studies on hydrogen supply chains and studies estimating energy use of data centers and AI workloads (review synthesis; specific lifecycle analyses and energy-use studies referenced in paper).
The convergence of green finance and computing — especially automated ESG assessment — expands monitoring capacity but also amplifies measurement divergence and greenwashing risks.
Review of literature on automated ESG tools, sustainable finance, and computational assessment methods (synthesis of empirical and conceptual studies; no single sample size reported).
AI and digitalization are restructuring labor markets, producing wage polarization and rents, with outcomes mediated by labor-market institutions.
Review of labor-market literature on AI/digitalization effects (aggregate synthesis of empirical studies and theoretical papers; review does not report an aggregated sample size).
Progressing from ChatGPT 3.5 to 4.0 produced three distinct effect scenarios across markets, which reinforce the paper's inflection point conjecture.
Empirical comparison/analysis of the effects associated with different ChatGPT versions (3.5 vs 4.0) on online labor markets; method implied to be similar DiD or temporal comparison. (Specific sample sizes and the definitions of the three scenarios are not provided in the abstract.)
The authors developed a Cournot competition model that identifies an inflection point for each market: before this point human workers benefit from AI enhancements; beyond this point human workers would be replaced.
Theoretical modeling via a Cournot competition framework constructed by the authors to characterize market dynamics and derive an inflection point; this is a model-based (analytical) result rather than an empirical estimate.
AI drives changes in economic growth.
The paper synthesizes theoretical and empirical arguments from the literature about AI's role for economic growth; the review itself does not present new growth accounting or causal estimates.
AI influences income and wage disparity.
Review discussion of research linking technological change and differential wage/income outcomes; no original econometric analysis or dataset presented in this paper.
AI adoption affects productivity levels.
Discussion and synthesis of existing economic literature on AI and productivity included in the review; the paper does not report primary empirical estimates or a quantified effect size.
Education systems, training/reskilling, labor market institutions, industrial policy, and social safety nets mediate the net employment outcomes of AI adoption.
Policy and institutional analysis grounded in labor economics theory; presented as a mediating mechanism in the synthesis rather than demonstrated with empirical causal estimates or sample-based intervention studies.
Knowledge industries exhibit significant complementarities as AI augments cognitive tasks, although some research and analytical roles may be automated.
Theory-based assessment of cognitive-task complementarity and substitution; synthesis rather than empirical occupational-level measurement or causal estimates provided in the paper.
In services, routine service tasks are vulnerable to AI, while high-contact and creative services are less vulnerable; digital platform services are likely to expand.
Task-level sectoral reasoning and qualitative examples in services; no empirical sectoral employment dataset or quantified vulnerability scores reported in the paper.
Manufacturing has strong automation potential but also opportunities in advanced manufacturing and maintenance/engineering roles.
Sector-specific analysis combining task vulnerability to automation with emergence of advanced manufacturing tasks; presented as theoretical/qualitative assessment rather than measured manufacturing employment trajectories from a stated sample.
Distributional effects will include wage polarization (rising returns to high-skill labor and pressure on middle-skill wages) and uneven regional impacts.
Application of SBTC and task-based wage theory to AI adoption; sectoral and regional heterogeneity discussed qualitatively. No new wage-distribution panel or cross-country regression evidence reported in the paper.
Short- to medium-run transitional unemployment, wage polarization, and sector- and country-level heterogeneity are likely.
Temporal-mismatch argument from task-based substitution and SBTC frameworks; sectoral assessment across manufacturing, services, knowledge industries. Evidence is theoretical/synthesized rather than from a stated empirical panel or cross-sectional dataset.