Evidence (3224 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| 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 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| 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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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 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%).
Three contributions are presented: the Agentic AI Framework (AAF 3.0); a cross-domain synthesis formalising the inverse evidence–complexity relationship; and a phased sociotechnical roadmap integrating governance sequencing, reimbursement reform, and equity safeguards.
Descriptive claim about the paper's outputs. These contributions are stated in the abstract as the study's deliverables based on the narrative review and synthesis of 81 sources.
Agentic AI is defined as autonomous, goal-directed systems capable of multi-step workflow coordination.
Definition provided by the authors within the paper (conceptual framing used for the review).
This structured narrative review of 81 sources (2020–2025) evaluates whether Agentic AI ... can support structural adaptation in ageing health systems.
Methodological statement in the paper: the study is a structured narrative review of 81 sources from 2020–2025.
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.
The paper identifies key research gaps and proposes a future research agenda focused on human–AI interaction, organizational governance, and ethical accountability.
Conclusions/recommendations from the conceptual meta-analysis (paper-generated research agenda; no empirical testing reported in abstract).
This study presents a conceptual meta-analysis of interdisciplinary literature on AI-augmented decision-making in organizations.
Methodological statement of the paper (the paper itself is a conceptual meta-analysis); no primary empirical sample reported in the abstract.
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).
The study employs the Difference-in-Differences (DiD) method to estimate AI impacts on online labor markets over time.
Methodological statement in the abstract specifying the use of Difference-in-Differences for empirical identification; implementation details (controls, parallel trends checks, sample size) are not given in the abstract.
The Act instituted a rigid seven-percent per-country cap that allocates the same number of visas to India (population of 1.4 billion) as to Iceland (population of 400,000).
Statutory per-country cap (7% rule in the INA) combined with publicly available country population figures for India and Iceland; claim about identical allocation follows directly from the 7% rule.
The Immigration Act of 1990 established a ceiling of 140,000 employment-based green cards annually.
Statutory fact derived from the Immigration Act of 1990 and the Immigration and Nationality Act (INA) provisions setting employment-based annual numerical limits.
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.
Research agenda: there is a need for causal studies on AI’s impact on accounting labor demand and firm performance, analyses of distributional effects across firm sizes and industries, and evaluation of regulatory frameworks for reliable, interpretable AI in financial reporting.
Author-stated research priorities drawn from gaps identified in the literature review; not an empirical finding.
Policy implications include workforce retraining, standards for AI auditability and transparency, and regulation balancing innovation and controls (privacy, fraud prevention).
Policy recommendations based on identified risks and barriers discussed in the paper rather than empirical policy evaluation.
For stronger causal evidence, recommended empirical methods include difference-in-differences on adopting firms vs. controls, matched samples, and randomized pilots for particular tools, supplemented by qualitative interviews.
Methodological recommendations stated in the paper (not an empirical finding); no implementation/sample reported in the abstract.
The paper's evidence is policy‑oriented, qualitative and analytical; it does not report causal estimates from new field data and produces testable propositions and an empirical agenda instead.
Explicit methods statement in the paper: structured desk review, corridor process mapping, governance gap analysis; absence of field experiments or causal quantitative analysis.
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).
The empirical approach tests for common long-run relationships across patenting series and identifies structural breaks concentrated after 2010.
Description of empirical strategy: time-series econometric analysis of patent filing series (1980–2019) including tests for common long-run relationships (cointegration) and structural break detection. The paper reports results of these tests (presence/absence of common trends and timing of breaks).
The paper highlights governance risks requiring transparency about LLM-derived mappings, mitigation of model biases, privacy-preserving data practices, and careful communication of uncertainty to avoid overconfident policy recommendations.
Explicit discussion of risks and governance considerations in the paper; this is an acknowledgment rather than an empirical claim. No implementation or audit evidence is provided.
Backtesting the architecture on historical automation waves and recent AI introductions will validate model design and calibration.
Paper explicitly proposes backtesting and holdout validation using historical automation episodes and recent AI adoption events; does not report completed backtests or empirical sample sizes.
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).
The study used a cross-sectional quantitative survey (purposive sampling) of pharmaceutical-sector employees in Karnataka, India (N = 350) and analyzed relationships using PLS-SEM (SmartPLS 4.0).
Study design and methods as reported in the paper summary: cross-sectional survey, purposive sampling, N = 350, analysis via Partial Least Squares Structural Equation Modeling (SmartPLS 4.0).
The paper calls for subsequent quantitative validation (using task-based, matched employer-employee, and provider-level panel data) to estimate causal impacts on productivity, health outcomes, wages, and employment composition across the three interaction levels.
Stated research agenda and measurement recommendations in the paper's discussion section.
The study is qualitative and small-sample (four case) and therefore interpretive and illustrative rather than statistically generalizable.
Explicit methodological statement in the paper: design = qualitative multiple case study, sample = four AI healthcare applications.
The study identifies a three-level taxonomy of human–AI interaction in healthcare: AI-assisted, AI-augmented, and AI-automated.
Conceptual taxonomy derived from multiple qualitative case studies (n=4) using cross-case comparison and Bolton et al. (2018)'s three-dimensional service-innovation framework.
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.
There are few large-scale randomized controlled trials (RCTs) showing direct patient outcome improvements from GenAI CDS; high-quality real-world and longitudinal studies are limited but essential.
Evidence-maturity statement in the paper summarizing the literature; the paper explicitly notes scarcity of large RCTs and longitudinal evaluations.
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.
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).
Experimental structure determination (X‑ray, NMR, cryo‑EM) remains the gold standard but is slow, costly, and low‑throughput.
Paper explicitly states experimental methods are 'gold standard' and characterizes them as slow, costly, low‑throughput; the PDB is cited as the source of structural ground truth.
The authors did not perform primary empirical validation or simulation of TVR‑Sec across real VR deployments.
Methods and limitations section explicitly state no original empirical experiments or simulations were conducted; analysis is conceptual and qualitative.
The paper's scope comprised a comparative literature review and conceptual integration of 31 peer‑reviewed studies published between 2023 and 2025.
Authors' methods description specifying sample size and publication window: 31 peer‑reviewed studies (2023–2025).
There is a need for empirical research quantifying earnings dispersion, labor substitution effects, and the welfare impacts of GenAI-driven content economies over time.
Explicit research recommendation made in the paper based on gaps identified during analysis of the 377 videos (study is qualitative and does not measure these outcomes).
The analysis identifies ten shared use cases that creators present as pathways to income using GenAI.
Coding of the 377-video corpus resulted in a catalog of ten use cases (as reported in the paper).
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).