Evidence (3470 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 |
Org Design
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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.
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.
Deterministic automated verifiers provide objective pass/fail checks for task success.
Methods section: verifiers are deterministic and automated, enabling objective evaluation of whether an agent's trajectory accomplished the task.
Scale of experiments: seven agent–model configurations and 7,308 execution trajectories were used to compute pass rates and deltas.
Reported experimental scale in Methods: 7 agent–model configurations and a total of 7,308 agent execution traces collected and analyzed across tasks/conditions.
Each task was evaluated under three conditions: (1) no Skills, (2) curated (human-authored) Skills, and (3) self-authored (model-generated) Skills.
Experimental protocol described in Methods: three-arm evaluation per task across the SkillsBench benchmark.
SkillsBench benchmark: evaluates 86 tasks spanning 11 domains with deterministic, automated verifiers.
Dataset and benchmark description in the paper: SkillsBench contains 86 tasks across 11 domains and uses deterministic pass/fail verifiers for objective evaluation.
Framing claim: Ideological contests typically produce opposing normative visions (e.g., collectivized command economies vs. market democracies), which makes the development of Western economic theories that portray markets and democracy as dysfunctional puzzling.
Framing and motivation provided in the paper's introduction and background sections; synthesis of conventional expectations about ideological contest outcomes.
The paper uses a qualitative case‑study approach (archival and textual analysis, contextualization, interpretive synthesis) rather than attempting exhaustive quantitative causal identification.
Explicit methods description in the paper: in‑depth historical/institutional examination, archival/textual work, and interpretive synthesis.
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 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.
The paper's empirical scope is primarily conceptual/theoretical and literature‑based rather than an empirical case study or large‑scale data experiment; it emphasizes the need for future empirical validation.
Explicit methodological description within the paper stating reliance on literature review and conceptual development; absence of empirical sample or case study.
Typical evaluation metrics reported are accuracy, precision, recall, F1-score, AUC, detection rate, false positive rate, latency, and computational cost.
Survey of evaluation practices in reviewed papers listing the metrics authors commonly report.
Emerging approaches in the literature include federated learning, online/streaming learning, and transfer learning for cross-device generalization.
Trend analysis across recent papers indicating adoption of federated and continual learning paradigms and transfer-learning techniques.
Unsupervised and semi-supervised methods (clustering, one-class classifiers, autoencoder-based anomaly detectors) are commonly employed to handle unlabeled/anomalous IoT traffic.
Synthesis of studies using anomaly-detection paradigms and unsupervised techniques reported in the reviewed papers.
Deep learning approaches used include CNNs, RNNs/LSTMs for sequence/traffic analysis, and autoencoders for anomaly detection.
Surveyed literature and taxonomy noting multiple studies that apply convolutional and recurrent architectures and autoencoders to network/traffic data.
Common ML approaches reported for IoT IDS include supervised models (random forest, SVM, gradient boosting, neural networks).
Taxonomy and literature synthesis showing frequent use of classical supervised classifiers in surveyed papers and experiments.
Empirical research suggestion: recommended outcome variables for future empirical work include productivity (TFP), profitability, exports, employment composition, and process innovation rates; explanatory variables include AI adoption intensity, strategic alignment indices, leadership commitment surveys, sensing activities, and institutional support measures.
Explicit research agenda and measurement suggestions provided in the paper based on the framework and gaps identified in the 72‑article review.
Scope & limits: the paper is a literature synthesis (no new primary empirical data), has a geographical emphasis on Ibero‑America, and covers literature up to 2024 (may omit post‑2024 developments).
Explicit limitations and scope noted in the paper (no primary data; regional emphasis; time window).
Methodological approach: the paper uses a structured narrative literature review following Torraco (2016) and Juntunen & Lehenkari (2021), analyzing a corpus of 72 articles from 2015–2024 via thematic synthesis and systematic coding.
Explicit methodological statement in the paper specifying approach, corpus size (72 articles), time window (2015–2024), and analytic techniques (thematic synthesis and coding).
The framework yields eight empirically testable propositions linking capability development to firm outcomes (the paper explicitly lists eight propositions including P1–P3 and five additional linked propositions).
Explicit claim in the reviewed paper: framework includes eight testable propositions; propositions are theoretical and untested empirically within the paper.
The review followed PRISMA guidelines and included 30 scholarly articles retrieved from Scopus, published between 2020 and 2025, selected using pre-specified inclusion criteria.
Methods section of the paper reporting the SLR protocol, database, time window, and number of included studies.
These quantitative performance figures come from case‑level, high‑performer pilots and should not be treated as typical industry benchmarks.
Authors' caveat based on the composition of evidence in the review (skew towards pilots and selected advanced implementations; limited longitudinal/multi‑project empirical studies).
Inter‑rater reliability for the study selection/encoding was Cohen’s κ = 0.83 (substantial agreement).
Reported inter‑rater reliability statistic from the review's quality control step (Cohen's kappa = 0.83).
The review screened 463 Scopus records (2018–2026) and selected 160 peer‑reviewed studies using a PRISMA‑guided process.
Systematic literature review described in paper: Scopus search (2018–2026), PRISMA screening and eligibility filtering; initial n=463, final n=160.
The abstract does not report the study sample size, sectoral scope, or country/context—limiting assessment of external validity and generalizability.
Observation of reporting in the paper's abstract (absence of sample size, sectoral/country context information in the abstract as provided).
The study used a two-stage mixed-methods design: a qualitative exploratory phase to surface determinants of trust and inertia, followed by a quantitative phase to validate the conceptual framework.
Methods description in the paper: explicit two-stage mixed-methods approach (qualitative then quantitative) used to identify and test determinants of initial trust and inertia toward GAICS.
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).
Economy & Finance threads contained no self-referential content, suggesting agents can engage in market discussion without representing themselves as agents.
Topic-model-derived topical category labeling and tagging for self-referential themes showing zero instances of self-reference in posts categorized as Economy & Finance in the dataset; counts derived from the 361,605 posts.
The authors released their code and data for reproducibility at https://github.com/blocksecteam/ReEVMBench/.
Statement in the paper indicating public release of code and dataset at the provided GitHub URL.
The workshop identifies specific research directions for AI economics: cost–benefit and ROI analyses of shared infrastructure; market design for procurement of co-designed systems; models of innovation incentives under different IP/data-governance regimes; labor market impact assessments; and empirical studies of how validation ecosystems affect adoption rates and pricing.
Explicitly listed research directions in the workshop summary and roadmap produced by consensus at the NSF workshop (Sept 26–27, 2024).
The workshop's findings are based on qualitative synthesis of expert judgment and stakeholder inputs rather than primary empirical data or controlled experiments.
Explicitly stated in the Data & Methods section of the workshop summary; methods: expert panels, thematic breakout sessions, cross-disciplinary discussions, consensus-building.
The workshop convened researchers, clinicians, and industry leaders to address co-design across four thematic areas: teleoperations/telehealth/surgical operations; wearable and implantable medicine; home ICU/hospital systems/elderly care; and medical sensing/imaging/reconstruction.
Workshop agenda and participant list from the two-day NSF workshop (Sept 26–27, 2024); methods included thematic breakout sessions focused on these four areas. Documentation at https://sites.google.com/view/nsfworkshop.
The paper uses a mixed-methods approach combining a systematic literature review with an empirical practitioner survey to assess perceptions, adoption, and impact of AI-driven tools.
Methodological statement in the paper; survey design covers tool usage, perceived benefits, challenges, and expectations.
Because this is a conceptual/systems-architecture paper, it does not present new empirical performance benchmarks.
Explicit statement in the paper's Data & Methods section that no new empirical benchmarks are presented.
DPS was empirically evaluated across diverse reasoning domains (mathematical reasoning, planning, and visual-geometry) to test generality.
Paper reports experiments on those three categories of tasks; they are listed as the evaluated tasks in the methods/experiments section.
DPS uses the inferred per-prompt state distributions as a predictive prior to select prompts estimated to be most informative, avoiding exhaustive candidate rollouts for filtering.
Method and selection mechanism described: predictive prior ranking/filtering replaces rollout-heavy candidate evaluation. (Procedure described in paper; empirical comparisons reported.)
Dynamics-Predictive Sampling (DPS) models each prompt’s "extent of solving" under the current policy as a latent state in a dynamical system (a hidden Markov model) and performs online Bayesian inference on historical rollout reward signals to estimate that state.
Methodological description in the paper: DPS uses an HMM representation of per-prompt solving progress and applies online Bayesian updates using past rollout rewards. (No numerical sample size needed for this modeling claim.)
The authors recommend specific measurement metrics and empirical research priorities (e.g., MAPE, stockout frequency, inventory turns, lead times, fill rates, total supply chain cost, service-level volatility, resilience measures; causal studies like diff-in-diff or randomized interventions).
Explicit recommendations in the paper's measurement and research agenda sections.
The study's small sample size and qualitative design limit external generalizability and prevent causal effect size estimation; potential selection and reporting biases exist due to purposive sampling and interview-based data.
Authors explicitly state these limitations in the paper's limitations section.
The study is a qualitative multi-case study of five medium-to-large organizations, using semi-structured interviews across procurement, production planning, inventory management, and distribution, analyzed via cross-case comparison.
Methods section description provided by the authors (sample size n = 5, sectors, interview-based primary data, cross-case analysis).
There is limited empirical causal evidence linking specific explanation types to long-term outcomes (safety, fairness, economic performance) in real-world deployments.
Meta-level finding of the review: authors report gaps in the literature—few causal or longitudinal studies of explanation interventions in deployed, high-stakes settings.
The literature groups explainability impacts along three linked dimensions — user trust, ethical governance, and organizational accountability.
Analytical result of the review's thematic coding and synthesis across interdisciplinary literature (categorization derived from the reviewed corpus).
The paper is primarily theoretical and prescriptive: it synthesizes literature and proposes a framework and design guidelines rather than reporting large-scale empirical datasets or causal identification of economic outcomes.
Meta-claim about the paper's methods explicitly stated in the Data & Methods summary; based on the paper's methodological description.
Key measurable outcomes to assess Human–AI teams include accuracy/efficiency, robustness to novel cases, decision consistency, trust/misuse rates, training costs, and inequity indicators.
Prescriptive list of metrics offered by the authors as part of the research agenda and evaluation guidance; not empirically derived from a dataset in the paper.
Empirical evaluation strategies for Human–AI teams should include randomized interventions, field trials, lab experiments, phased rollouts (difference-in-differences), and structural models that allow interaction terms between human skill and AI quality.
Methodological recommendation in the paper; suggested study designs rather than implemented analyses.