Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
The study's empirical identification relies on longitudinal variation with city fixed effects and time effects, plus non-linear/threshold identification via polynomial (DE^2) terms and threshold-regression using green-technology-innovation as the threshold variable.
Description of empirical strategy in the paper: panel fixed-effects models (controlling for time-invariant city heterogeneity and common time shocks), mediating-effect models for channel tests, and threshold-regression models for regime-dependent effects, applied to the 278-city 2011–2022 panel.
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).
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.
Kebumen UNESCO Global Geopark is used as a practical context to ground the framework; its ecological/cultural assets and emergent digital presence make it a suitable case for studying emerging destinations balancing innovation with authenticity.
Paper provides Kebumen Geopark as the illustrative case study/context for the conceptual framework; no systematic case-study data reported.
Operationalization suggestions: social proof via ratings, reviews, UGC volume and valence; behavioral proxies include bookings and inquiries as outcomes.
Paper explicitly lists social-proof indicators and behavioral proxies as part of recommended empirical approaches (digital-trace and platform data).
Operationalization suggestions: sustainability communication via message clarity, perceived authenticity, and specificity of eco-actions.
Operationalization guidance in the paper for measuring sustainability messaging in experiments/surveys.
Operationalization suggestions: AI personalization via perceived relevance, transparency, and perceived fairness of recommendations.
Operationalization guidance in the paper; proposed as latent construct indicators for future SEM or experiments.
Operationalization suggestions: digital experience quality via usability, information richness, responsiveness, multi-channel integration.
Operationalization guidance provided in the paper's methods suggestions; intended for future empirical measurement.
Recommended empirical follow-ups include Structural Equation Modeling (SEM), experimental tests (lab/field/online), quasi-experimental causal-inference methods (DiD, IVs, RD), comparative/regional designs, and analysis of digital-trace/platform data (clickstreams, recommendation logs, bookings, UGC).
Methodological recommendations explicitly listed in the Data & Methods and Research Agenda sections of the paper; no primary empirical work conducted.
The framework produces ten testable propositions mapping hypothesized direct and mediated links among constructs and specifying contingencies for future empirical testing.
Explicit statement in the paper that the framework yields ten testable propositions; no empirical validation reported.
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).
This study is descriptive and comparative rather than quantitative; it relies on available policy documents and secondary literature rather than original field interviews or measured outcomes.
Explicit methodological statement in the paper listing qualitative document analysis, comparative literature review, and policy commentary; limitation acknowledged by authors.
A research agenda for AI economics should include: formalizing consent as a transaction/contracting problem; empirical RCTs and natural experiments measuring effects of consent designs; mechanism design for privacy-preserving data sharing; and policy evaluation of consent regulations.
Explicitly listed research directions in the workshop outputs and position papers; these are proposed next steps rather than empirical findings.
Follow-up empirical methods should include qualitative interviews, focus groups, usability studies, field experiments (A/B tests), and policy/legal-technical assessments.
Recommended research methods enumerated in the workshop outputs and position papers; these are proposed future methods rather than findings from conducted studies.
The Futures Design Toolkit (scenario planning, persona generation, speculative design) was used as a primary method in the workshop.
Methodological description in the workshop summary listing the Futures Design Toolkit and associated activities; procedural claim rather than empirical.
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.
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).
Risk and ambiguity manipulations: risk condition communicated a single explicit leak probability of 30%; ambiguity condition communicated the leak probability as a range (10–50%).
Paper's methods section describing the manipulations used in the randomized experiment (N = 610); these specific probability framings were the core independent-variable manipulations.
Experimental design: study used a 2 × 3 between-subjects design with N = 610, crossing information environment (Risk vs Ambiguity) with privacy-treatment conditions (including privacy-threatening vs neutral and different data-type labels).
Methodological description reported in the paper: participants (N = 610) randomized across 6 experimental arms derived from the 2 (Risk vs Ambiguity) × 3 (privacy treatments) factorial design; tasks included choosing between a standard product basket and an AI-personalized basket.
When leak probabilities are known (risk condition: explicit 30% leak probability), adoption of personalization is about 50% and is not significantly affected by privacy-threatening versus neutral information.
Same randomized experiment (N = 610) with a risk manipulation that explicitly stated a single 30% leak probability. Measured adoption rates showed roughly 50% uptake and no statistically significant difference between privacy-threatening and neutral conditions under risk.
Many apparent inter-domain differences vanish once measurement uncertainty is accounted for.
Bootstrap confidence intervals and repeated-sample comparisons showing that differences in citation share or prevalence observed in single-run snapshots are often not statistically significant when uncertainty from repeated sampling is included.
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).
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.
Because the sample is small and purposive and the design is qualitative, insights are rich but not statistically representative or quantified across the broader research landscape.
Authors' stated study limitations in the paper acknowledging small purposive sample (n=16) and qualitative design.
The study's data come from semi-structured interviews with 16 expert practitioners across biosecurity, cybersecurity, education, and labor.
Study methods reported in the paper: qualitative data source explicitly stated as 16 semi-structured interviews across listed domains.
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.
Crystallization Efficiency (CE) is defined as Useful_Crystallized_Knowledge / (Human_Effort × Time).
Operational formalism and metric definitions presented in the paper (explicit formula provided). This is a proposed metric, not an empirically validated measure.
The paper proposes operational patterns (Dual-Workspace Pattern separating live interaction workspace and persistent knowledge workspace) and a Spiral Development Model (iterative interaction → crystallization → validation → redeployment).
Operational framework section describing patterns and workflows; illustrated in the case study implementation.
The Knowledge Crystallization Cycle formalizes operations (extract, synthesize, validate, integrate) and proposes efficiency and quality metrics including Crystallization Efficiency (CE), Fidelity, Reuse Rate, and Freshness/Volatility Score.
Operational formalism section of the paper presenting metric definitions and proposed calculations (e.g., CE = Useful_Crystallized_Knowledge / (Human_Effort × Time)). These are proposed metrics, not validated at scale.
The paper introduces a Three-Layer Cognitive Architecture that organizes agent knowledge by volatility and degree of personalization (stable/core knowledge; institutionalized heuristics/patterns; volatile/session-level tacit details).
Architectural specification presented in the paper (conceptual design document). No experimental validation beyond the illustrative case study.
Nurture-First Development (NFD) reframes agent creation from a one-time engineering task into a continuous, conversational growth process.
Conceptual formalization in the paper (architectural and operational descriptions). No large-scale empirical test reported; supported by theoretical argumentation and illustrative examples.