Evidence (1286 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 |
Inequality
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The paper's findings are based on a combination of literature review, data analysis, and an empirical study involving HR professionals.
Methodological description given in the paper's summary (no further methodological details, sample size, instruments, or statistical methods provided in the summary).
The study draws extensively on contemporary literature in sustainable supply chain management, healthcare procurement, and ESG governance.
Methodological claim about the paper's research approach: literature review/synthesis across the cited domains (bibliographic evidence within the paper).
The paper empirically analyzes the algorithm-automated versus human decision-making debate using the AST and STS theoretical lenses.
Theoretical analysis and empirical synthesis across the reviewed studies (n=85), explicitly stated use of AST and STS frameworks to interpret findings.
To address the duality of benefits and harms, the paper proposes a dynamic Human-in-the-Loop (HITL) model that reconciles algorithmic determinism with normative HRM demands.
Conceptual/theoretical contribution presented in the paper (proposed HITL model based on synthesis of findings and theory).
There is substantial heterogeneity in effects (I^2 = 74%), indicating variability across studies.
Meta-analytic heterogeneity statistic reported in the paper (I^2 = 74%).
The results presented in the paper are based on a literature recherche, an analysis of individual tasks across different occupations (conducted within Erasmus+ projects), and discussions with trainers/educators.
Methodological statement from the paper; indicates the types of evidence used. The abstract does not provide numbers for analyzed tasks, the number of occupations, details of Erasmus+ projects, or counts of trainers/educators consulted.
Research has insufficiently modeled joint distributional outcomes and environmental performance, and lacks integrated evaluation of AI-enabled sustainable finance under heterogeneous disclosure regimes.
Review-level identification of methodological gaps across the surveyed literature (authors' synthesis of existing studies and their limitations).
There is a shortage of long-horizon causal evidence on non-linear coupling between digitalization and decarbonization, limiting robust policy inference.
Meta-level assessment in the review noting gaps in existing empirical literature (review authors' synthesis of the field; claim about research availability rather than primary data).
A Job Digital Intensity Index (JDII) was constructed to capture how digitally intensive jobs are overall, based on the range of digital tasks performed.
Methodological construction described in the report using ESJS digital task items to form a composite JDII.
Research should prioritize dynamic, task-based models that include transitional frictions, heterogeneous agents, and sectoral structure to better measure AI exposure and impacts.
Methodological recommendation grounded in the paper's theoretical critique of static occupation-level automation metrics and noted empirical gaps.
Timing uncertainty and measurement challenges make forecasting the pace and scale of AI-induced employment change inherently uncertain.
Methodological limitations section noting uncertainty in AI adoption speed and difficulties mapping capabilities to tasks and predicting new occupation emergence.
Personal data are nonrivalrous and highly replicable, so selling data does not follow ordinary scarcity logic.
Analytic/property claim about the economic characteristics of digital information; supported by conceptual definitions and common technical facts about data replication; no empirical sampling needed.
Empirical approach measured and compared expectation formation, innovation responses, and pipeline outcomes across local exposure to closures and across distinct entrepreneurial identity groups.
Methodological description: survey-based, cross-country quantitative approach using measures of local exposure (nearby closures), identity classification (family/purpose-driven vs. wealth-driven), and outcomes (expectations, perceived impediments, self-reported innovation, pipeline transitions) in a sample >27,000.
The study analyzes a cross-country sample of more than 27,000 entrepreneurs across 43 countries (survey-based, comparative).
Descriptive claim about the dataset used in the paper: survey-based sample size >27,000 spanning 43 countries as reported in Data & Methods.
The empirical strategy uses baseline panel regressions with standard controls (e.g., firm size, performance, leverage) and fixed effects to estimate the AI → pay relationship.
Methods section describing regression specifications including firm controls and fixed effects applied to the A-share firm panel.
Data consist of a panel of Chinese A-share listed companies covering 2007–2023.
Data description in the paper specifying the sample period and population (A-share listed firms, 2007–2023).
The firm-level AI application indicator is constructed via textual analysis of corporate disclosures (e.g., filings/annual reports) to capture AI application intensity.
Methodological description in the paper describing text-based construction of an AI application indicator from corporate disclosures for listed firms in the 2007–2023 sample.
Calibration via Method of Simulated Moments (MSM) matches six empirical moments to discipline mechanism magnitudes.
Model calibration procedure reported in the paper: MSM matching six chosen empirical moments that summarize key pre/post-AI patterns (paper states six moments were used).
Empirical validation of the integrated Kondratieff–Schumpeter–Mandel framework requires firm-level adoption and profitability data, sectoral investment series, and cross-country comparisons using panel methods and identification strategies (e.g., diff-in-diff, IV).
Methods/limitations section recommendation (explicitly states no single micro-econometric identification strategy was reported and outlines required data/methods).
The three frameworks (Kondratieff, Schumpeter, Mandel) are complementary: Kondratieff frames periodicity, Schumpeter provides micro-mechanisms of innovation-driven change, and Mandel foregrounds socio-political constraints and distributional outcomes.
Conceptual integration and comparative theoretical analysis (qualitative synthesis).
Kondratieff's framework is useful for identifying broad periodicities (recurring phases of expansion and stagnation) in capitalist development but is less specific about microeconomic mechanisms.
Theoretical review of Kondratieff literature and conceptual assessment (qualitative).
Non-probability sampling and self-reported measures limit claims about prevalence and causality; cross-sectional design cannot capture dynamics of skill acquisition over time.
Study limitations explicitly reported by authors: non-probability sampling, self-reported measures, and cross-sectional design.
The study is primarily diagnostic and prescriptive rather than empirical: no explicit empirical dataset, causal identification strategy, or statistical estimation is reported.
Methods section of the paper explicitly characterizes the work as conceptual, systems-oriented, and not reporting empirical evaluation data.
The urban AI index is constructed via text-mining techniques to capture city-level AI capability/intensity.
Methodological description: authors report using text-mining to build a city-level AI capability/intensity index (details of sources and text-mining procedure not provided in the summary).
The digital trade index is constructed using the entropy-TOPSIS method (multi-indicator aggregation).
Methodological description: digital trade index aggregation via entropy-TOPSIS reported by authors.
Research recommendation: invest in longer-run, rigorous impact evaluations (RCTs, panel studies) and system-level assessments to capture spillovers and sustainability outcomes.
Authors' stated research agenda based on identified methodological gaps (limited long-term and system-level evidence) in the review.
There is variation in study design and quality in the evidence base (RCTs, quasi-experimental studies, observational case studies, pilots).
Methodological caveats noted by the authors summarizing the diversity of designs reported across reviewed studies.
The review used a structured literature review with thematic synthesis and a comparative effect-size analysis to quantify ranges for yield, cost, and efficiency outcomes.
Authors' description of review approach and analytical methods in the Data & Methods section.
The evidence base reviewed comprises more than 60 peer-reviewed articles and institutional reports from 2020–2025, primarily focusing on Sub-Saharan Africa.
Statement in the paper's Data & Methods section describing the scope and composition of the review sample.
Effect sizes and impacts vary substantially across contexts—by crop, farm size, and institutional setting.
Comparative synthesis across studies showing heterogeneity in reported outcomes and authors' methodological caveats highlighting context dependence.
Technologies assessed in the review include predictive analytics, digital advisory systems, smart irrigation, pest/disease detection, and precision fertilization.
Descriptive synthesis of the types of AI and digital technologies evaluated across the >60 reviewed articles and reports (2020–2025).
The study has potential selection and ecological-validity constraints because it was conducted at two institutions across six courses, limiting generalizability.
Authors note limitations regarding sample scope (two institutions, six courses) and the ecological validity of the experimental tasks/settings.
The study employed a multi-method approach combining experimental quantitative analysis (descriptives, GLM, non-parametric robustness checks) with qualitative topic-based coding of open-ended survey responses.
Methods description: randomized/experimental assignment; quantitative analyses using GLM and non-parametric tests; qualitative topic-based coding of student responses; sample N = 254 across six courses at two institutions.
The study did not directly measure accessibility or impacts on students with disabilities, though qualitative results suggest possible intersections with inclusive and multimodal learning design.
Limitation stated by authors: no direct measurement of accessibility outcomes; qualitative responses hinted at potential relevance to inclusive design but no empirical measurement of disability-related impacts.
The study focused on short-term, knowledge-based tasks and did not measure long-term learning or retention.
Authors explicitly note as a limitation that the experimental tasks were short-term and knowledge-based and that long-term retention was not measured.
Empirical generalization across all climate-AI systems is constrained by heterogeneous data availability and proprietary models, limiting the ability to produce universal quantitative claims.
Stated methodological limitation in the paper, noting heterogeneous data and the proprietary nature of some models restrict broad generalization.
The paper does not provide granular quantitative estimates of the economic cost of infrastructural asymmetries in climate-AI.
Explicit limitation stated by the authors in the Methods/Limitations section.
Falsifiability condition for intermediation-collapse: If intermediary margins remain stable despite measurable declines in information frictions, the intermediation-collapse mechanism is falsified.
Stated empirical test in the paper that compares measured intermediary markups/margins to proxies for information frictions and AI-driven automation across affected sectors.
Falsifiability condition for Ghost GDP: If monetary velocity does not decline (or instead rises) as the labor share falls, the Ghost GDP channel is unsupported by the data.
Explicit falsification condition provided in the paper based on the model link labor share -> velocity -> consumption; suggested empirical test using monetary-velocity proxies and labor-share series from FRED.
Empirically, top-quintile households account for roughly 47–65% of U.S. consumption.
Calibration and reported quantitative scenarios in the paper using U.S. consumption concentration data (constructed from U.S. consumption/income micro- and macro-data sources referenced in the methods section).
Instrumental-variable (IV) estimation is used to address endogeneity of AI adoption and to identify causal effects on employment and wages.
Paper states IV identification strategy applied to the 38-country panel; robustness checks and alternative specifications reported (paper refers to instrument details in full text).
The AI Adoption Index is constructed as a composite measure combining enterprise investment in AI, AI-related patent filings, and workforce/firm surveys on AI use across 38 OECD countries (2019–2025).
Paper's methodological description of the index construction; data sources enumerated as investment, patenting, and survey measures over the panel period.
The paper is entirely theoretical/analytical and does not report an empirical dataset.
Paper methodology section and abstract state primary tool is an analytical economic model; no empirical data or sample sizes are reported.
The same formal framework can be interpreted as a firm-level model where human skill investment maps onto AI/chatbot investment decisions.
Paper provides an alternative interpretation and formally maps agent skill-investment choices into an analogous firm R&D/AI-capital decision problem within the same mathematical framework.
The systematic review followed PRISMA protocol and analyzed a corpus of 103 items (peer‑reviewed articles and institutional reports) published 2010–2024.
Explicit methodological statement in the paper describing PRISMA use and corpus size/timeframe.
The study is limited by being a single‑country case; contextual factors (regulatory regime, infrastructure capacity, procurement practices) may limit generalizability and the study emphasizes institutional and ethical analysis rather than quantitative measurement of economic impacts.
Explicit limitations reported in the paper summarizing scope and emphasis.
Methods used include qualitative interviews with researchers and administrators, observation/documentation of tool use, mapping of data flows and third‑party dependencies, and normative/legal analysis contrasting local practices with GDPR principles.
Methods section of the paper as reported in the provided summary.
The study's empirical basis is a qualitative case study centered on environmental science research in Chile that adopts the GDPR as an organizing normative framework.
Paper description of study scope and normative framing (methods and focus described in Data & Methods).
There is a need for validated administrative and firm-level data on AI adoption, workplace monitoring, and worker outcomes, and for evaluation of policy interventions (mandated impact assessments, transparency requirements, worker representation rules) using randomized or quasi-experimental designs where feasible.
Research and measurement priorities set out in the commentary based on identified gaps; prescriptive recommendation rather than evidence-based finding.
The paper is a policy and legal commentary/synthesis and not an empirical causal study; it does not provide microdata on employment or wage effects but identifies plausible channels and institutional dynamics.
Author-stated methodology and limitations section describing type of study and data sources; explicitly reports lack of primary empirical data.