Evidence (3231 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5921 claims
Human-AI Collaboration
5192 claims
Org Design
3497 claims
Innovation
3492 claims
Labor Markets
3231 claims
Skills & Training
2608 claims
Inequality
1842 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 738 | 1617 |
| Governance & Regulation | 671 | 334 | 160 | 99 | 1285 |
| Organizational Efficiency | 626 | 147 | 105 | 70 | 955 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 349 | 109 | 48 | 322 | 838 |
| Output Quality | 391 | 121 | 45 | 40 | 597 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 277 | 145 | 63 | 34 | 526 |
| AI Safety & Ethics | 189 | 244 | 59 | 30 | 526 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 106 | 40 | 6 | 188 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 79 | 8 | 1 | 152 |
| Regulatory Compliance | 69 | 66 | 14 | 3 | 152 |
| Training Effectiveness | 82 | 16 | 13 | 18 | 131 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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AI adoption is concentrated among large firms and in regions with high-speed broadband and proximity to data centers, particularly for software-intensive and cloud-based applications.
Descriptive analysis using administrative data and a nationally representative enterprise survey from Turkey (2021-2024).
Our baseline model finds evidence that AI is productivity enhancing.
Results from the paper's stated baseline empirical model using BEA industry-account-based measures; model specification described by authors.
A common response to these worries stresses that the goods derived from work can be found elsewhere, often in better activities, suggesting that the proliferation of AI-powered automation does not threaten the meaningfulness of people’s lives.
Description of a commonly offered counterargument in the literature and popular debate (conceptual/literature-summary; no empirical data or sample reported).
These divergences (between simulation and human data and across scenarios) provide crucial insights for the future design of human-centered AI agents.
Paper conclusion in abstract indicating practical implications and discussion of how divergences vary across contexts and what that implies for design.
With actual human subjects, AI attributes—particularly transparency—were much more impactful than personality traits.
Abstract reporting results from the human-subjects experiment (N=290) indicating AI attributes, especially chain-of-thought transparency, had greater impact.
In simulation experiments, personality traits and AI attributes were comparatively influential on outcomes.
Abstract claim summarizing simulation experiment results (based on the 2,000 simulated runs) that personality and AI attributes were influential.
The paper introduces a novel posted-price procurement model with coverage objectives for studying platform procurement of human input.
Methodological contribution declared in the paper: presentation of a new formal model (posted-price procurement with coverage objectives).
A small coalition of targeted low-cost workers who commit to a price floor forces the platform's total spending to change from logarithmic to linear in M.
Theoretical analysis within the model showing that when a targeted subset of low-cost workers commit to a minimum price, the asymptotic scaling of platform spending increases from logarithmic (in M) to linear (in M); proof-based, no empirical sample.
A research-degree-student survey showed high performance ratings across information reliability, theoretical depth and logical rigor, with pronounced ceiling effects on a 7-point scale, despite all participants already being frontier-model users.
Authors report results from a survey of research-degree students evaluating the scholar-bots on specified dimensions (information reliability, theoretical depth, logical rigor) using a 7-point scale and note ceiling effects; participants reportedly were experienced model users.
Recovered panel scores placed Scholar A between 7.9 and 8.9/10 and Scholar B between 8.5 and 8.9/10 under multi-turn debate conditions.
Paper reports numeric panel scores (ranges) for the two scholar-bots in multi-turn debate scenarios; scores are presented as recovered panel evaluations.
Appointment-level recommendations placed both bots at or above Senior Lecturer level in the Australian university system.
Authors state that appointment-level syntheses from assessors recommended both scholar-bots at or above the Senior Lecturer rank (Australian system); based on the experts' syntheses.
Across the preserved expert record, all review and supervision reports judged the outputs benchmark-attaining.
Authors report that the preserved set of expert review and supervision reports (from the three assessors) rated scholar-bot outputs as attaining the benchmark standards used for assessment.
The scholar-bots were deployed across doctoral supervision, peer review, lecturing and panel-style academic exchange.
Authors report deployment of the generated scholar-bots in multiple academic task contexts (doctoral supervision, peer review, lecturing, panel debates); reported as part of evaluation protocol.
We converted those systems into structured inference-time constraints for a large language model.
Authors describe a pipeline that transforms the extracted scholar reasoning artefacts into inference-time constraints applied to a LLM; presented as part of methods for the two scholar cases.
We extracted the scholarly reasoning systems of two internationally prominent humanities and social science scholars from their published corpora alone.
Authors report an extraction procedure applied to the published corpora of two named scholars; claim is descriptive of dataset and method (n=2).
Policy proposals including universal basic income, portable benefits, retraining programs, and AI taxation are viable mechanisms to manage the socio-economic transition associated with AI, and the paper assesses these proposals.
Paper states it evaluates these policy proposals drawing on empirical studies, reports, and historical analysis; the abstract does not report empirical tests or effectiveness estimates for these policies.
The distributional consequences of AI adoption will be shaped primarily by institutional factors—including labor market regulation, education policy, and corporate governance structures—rather than by the technology itself.
Argument based on a literature review drawing on recent empirical studies, industry reports, and historical analyses of past technological transitions; no new empirical estimate or sample size provided in the abstract.
AI differs from previous automation technologies in its capacity to perform cognitive and creative tasks.
Paper's conceptual claim supported by references to recent empirical studies and industry reports on generative AI and large language models; no specific sample size or quantified effect reported in the abstract.
The model's contribution lies in integrating four interdependent governance layers—technical, organizational, workforce, and regulatory—within a single labor-market framework.
Paper's stated conceptual contribution describing the four-layer governance model derived from the evidence map and synthesis.
Based on an evidence map of the included studies, we propose a hybrid governance model combining technical and organizational audits, inclusive upskilling/reskilling, participatory regulation, and responsible HR policies to align AI innovation with decent and inclusive work.
Conceptual proposal grounded in the paper's evidence map and qualitative synthesis of the 19 studies; model components explicitly listed in the text.
The evidence indicates that AI can support inclusion through assistive technologies and improved matching in labor-market settings.
Synthesis claim based on thematic analysis of the 19 included peer-reviewed studies (qualitative evidence across the corpus pointing to assistive technologies and improved matching as inclusion-supporting mechanisms).
The work holds important practical significance for promoting the coordinated and sustainable development of efficiency and fairness in the field of digital recruitment in China.
Concluding claim in abstract about practical significance and intended impact on efficiency and fairness; no empirical measures of nationwide impact provided.
These individual adaptation strategies provide important microlevel references for platform algorithm optimization and the improvement of relevant regulatory policies.
Paper's implication/discussion claim in abstract that findings can inform platform design and policy; presented as an application rather than empirically proven policy impact.
An empirical study revealed that active and targeted individual adaptation can effectively avoid the negative impact of algorithmic bias and significantly improve the overall job search success rates of different groups.
Statement in abstract reporting results of an empirical study conducted by the authors; however, the abstract does not report sample size, experimental design, statistical significance levels, or effect sizes.
A scientific four-in-one adaptation strategy system encompassing resume optimization, channel selection, proactive communication, and ability enhancement is constructed.
Paper's stated contribution: construction of a four-part adaptation strategy for job seekers described in abstract; no empirical validation details provided in abstract.
With the popularization of digital recruitment platforms in the era of artificial intelligence, algorithmic screening has become a core and indispensable component of talent matching in the modern labor market.
Statement in paper's introduction/abstract asserting widespread adoption of digital recruitment platforms and centrality of algorithmic screening; no specific adoption figures or data reported in the abstract.
The positive effect of supply chain digitalization on human capital structure is stronger for enterprises located in the eastern region of China.
Heterogeneity analysis in the paper using the DID framework on A-share listed companies (2013–2022); regional subsample analysis shows a larger effect in eastern China.
The positive effect of supply chain digitalization on human capital structure is stronger for enterprises operating in more competitive industries.
Heterogeneity analysis reported in the paper using DID on A-share listed firms (2013–2022); industry competition intensity is used to split sample and examine differential effects.
The positive effect of supply chain digitalization on optimizing human capital structure is stronger for enterprises facing higher external environmental uncertainty.
Heterogeneity analysis in the paper using the DID sample of A-share listed firms (2013–2022); authors report the effect is more pronounced under higher environmental uncertainty.
Supply chain digitalization enhances enterprises' capacity to absorb high-skilled labor by promoting the accumulation of digital intangible assets.
Mechanism analysis in the paper using DID on A-share listed companies (2013–2022); accumulation of digital intangible assets is cited as a channel increasing firms' demand/ability to hire high-skilled workers.
Supply chain digitalization enhances enterprises' capacity to absorb high-skilled labor by alleviating financing constraints.
Mechanism analysis reported in the paper using the quasi-natural experiment and DID approach on A-share listed firms; easing financing constraints is presented as one channel.
Supply chain digitalization enhances enterprises' capacity to absorb high-skilled labor by boosting public trust in brands.
Mechanism analysis in the paper using the DID design on A-share listed firms (2013–2022); brand/public trust is reported as a mediating channel.
Supply chain digitalization enhances enterprises' capacity to absorb high-skilled labor by increasing firms' market attention.
Mechanism analysis reported in the paper using the same DID framework and sample (A-share listed firms 2013–2022); market attention is listed as an identified channel through which digitalization affects human capital.
Supply chain digitalization drives the optimization of the human capital structure of enterprises.
Empirical analysis on A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2013 to 2022; authors treat pilots of supply chain innovation and application as a quasi-natural experiment and employ a difference-in-differences (DID) approach to identify the effect.
The paper proposes a conceptual framework of the underlying mechanisms of the LLM fallacy and a typology of its manifestations across computational, linguistic, analytical, and creative domains.
Author(s) contribution described in the paper (framework and typology); no empirical testing reported in the abstract.
The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing, programming, analysis, and multilingual communication.
Author(s) assertion based on literature review and conceptual overview; no empirical sample or experiment reported in the abstract.
Linking these measures to administrative data from 2012 to 2023 shows a broad shift from manual and digital toward frontier skills across occupations.
Longitudinal analysis linking OTSS to administrative labor market data covering 2012–2023, showing temporal changes in skill composition toward frontier skills.
We compute OTSS for all occupations in the German labour market.
Paper reports application of the OTSS metric across the set of occupations covering the German labour market.
Using natural language processing, generative AI and supervised machine learning, we develop an AI‐powered skill classification that enriches occupation‐linked skill labels with standardised GenAI‐generated descriptions and structured indicators of technological content, enabling transparent classification by technology intensity.
Paper describes methodological approach combining NLP, generative AI and supervised ML to create the skill classification and enriched labels.
This paper introduces a novel skill‐based measure of occupational technology intensity – the occupational technology skill share (OTSS) – that distinguishes between manual, digital and frontier technologies, including artificial intelligence (AI).
Paper statement of contribution / methodological development (description of new measure OTSS).
The future of Nagpur's industrial belt depends not on resisting automation, but on an aggressive reskilling strategy to bridge the gap between current workforce capabilities and future technological requirements.
Normative policy conclusion in the paper recommending reskilling as the primary response; based on the paper's analysis of task changes and projected role shifts; no program evaluation or empirical evidence of reskilling effectiveness reported in the excerpt.
There is a projected surge in demand for 'AI-collaborative' roles such as machine maintenance, data supervision, and process optimization.
Projection in the paper based on analysis of task complementarities between humans and AI, listing specific roles expected to grow; no quantitative demand estimates or sample sizes provided in the excerpt.
The paper documents 14 deliberate conservative assumptions — including frozen base GDP, no AI-on-AI compounding, a permanent friction floor, and conservative capture rates — all of which directionally understate the benefit.
Paper lists 14 conservative modeling assumptions and claims they bias results downward (i.e., understate potential benefits).
Even excluding demand expansion and robotics layers entirely, the direct productivity contribution alone reaches approximately $940 billion per year by 2036.
Model output reported in the paper when removing demand expansion and robotics layers.
In all four scenarios, cumulative net GDP exceeds cumulative AI infrastructure investment before 2036, with the base case achieving payback in 2033.
Model financial calculation comparing cumulative net GDP uplift to cumulative AI infrastructure investment across scenarios; explicit payback year reported for base case.
The base-case scenario yields approximately $1,057 billion in net annual GDP uplift by 2036, equivalent to 3.6 percent of 2024 GDP; the bear case produces $796 billion, the bull case $1,368 billion, and an agentic scenario produces $2,521 billion.
Model scenario outputs presented in the paper (four scenarios differentiated by capture rate and friction assumptions).
Sector-specific productivity gain percentages are anchored to published evidence, including a randomized controlled trial of GitHub Copilot (Kalliamvakou et al., 2023), JPMorgan CEO disclosures, and Cognizant's New Work New World 2026 research.
Paper states productivity percentages are anchored to published evidence and specifically cites Kalliamvakou et al. (2023) RCT, JPMorgan CEO disclosures, and Cognizant (2026).
AI has reshaped business operations and decision-making processes across commerce sub-sectors.
Stated in the paper as part of the literature- and trend-based review of AI adoption impacts (qualitative synthesis).
Proactive policy measures and organizational strategies are essential to ensure inclusive and sustainable employment growth in the AI-driven commercial environment.
Paper conclusion and policy recommendation based on the literature review and sector trend analysis (normative recommendation, not an empirical test).
The study emphasizes the growing importance of reskilling, upskilling, and human–AI collaboration for workforce adaptability.
Conclusion drawn from the literature and sectoral trend analysis highlighting policy and organizational implications (literature review/recommendation).