Evidence (8066 claims)
Adoption
5586 claims
Productivity
4857 claims
Governance
4381 claims
Human-AI Collaboration
3417 claims
Labor Markets
2685 claims
Innovation
2581 claims
Org Design
2499 claims
Skills & Training
2031 claims
Inequality
1382 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 417 | 113 | 67 | 480 | 1091 |
| Governance & Regulation | 419 | 202 | 124 | 64 | 823 |
| Research Productivity | 261 | 100 | 34 | 303 | 703 |
| Organizational Efficiency | 406 | 96 | 71 | 40 | 616 |
| Technology Adoption Rate | 323 | 128 | 74 | 38 | 568 |
| Firm Productivity | 307 | 38 | 70 | 12 | 432 |
| Output Quality | 260 | 71 | 27 | 29 | 387 |
| AI Safety & Ethics | 118 | 179 | 45 | 24 | 368 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 75 | 37 | 19 | 312 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 74 | 34 | 78 | 9 | 197 |
| Skill Acquisition | 98 | 36 | 40 | 9 | 183 |
| Innovation Output | 121 | 12 | 24 | 13 | 171 |
| Firm Revenue | 98 | 35 | 24 | — | 157 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 87 | 16 | 34 | 7 | 144 |
| Inequality Measures | 25 | 76 | 32 | 5 | 138 |
| Regulatory Compliance | 54 | 61 | 13 | 3 | 131 |
| Task Completion Time | 89 | 7 | 4 | 3 | 103 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 33 | 11 | 7 | 98 |
| Wages & Compensation | 54 | 15 | 20 | 5 | 94 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 27 | 26 | 10 | 6 | 72 |
| Job Displacement | 6 | 39 | 13 | — | 58 |
| Hiring & Recruitment | 40 | 4 | 6 | 3 | 53 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 11 | 6 | 2 | 41 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 6 | 9 | — | 27 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Failures of translation—both literal (across languages/markets) and metaphorical (between disciplines, scales, and practices)—impede global adoption and ideation of food products and innovations.
Argumentative synthesis citing cross-cultural examples and theoretical literature on translation costs; qualitative examples rather than empirical measurement of translation failures.
Industrial food R&D tends toward conservatism, privileging established measurement and classification schemes that can obscure sensory nuance and cultural variation.
Critical review and synthesis of literature on industrial R&D practices and measurement norms; illustrative industry examples cited; no systematic surveys or quantitative industry-wide data presented.
Language and conceptual frameworks (drawing on Wittgenstein) constrain what can be noticed, measured, and communicated about texture and taste, creating epistemic limits in scientific practice.
Philosophical analysis using Wittgensteinian language theory and examples from food science and sensory studies; literature synthesis and illustrative examples; no systematic empirical validation.
Empirical evidence shows that every 1 percentage Industrial Robot Density elevation leads to a 0.8 percentage point decrease in the Manufacturing Global Value Chain Participation Rate.
Empirical claim reported in the paper; method described as empirical analysis but the provided excerpt does not specify dataset, country sample, time period, model specification, controls, or sample size.
Developing countries face Technology Embargo, Rule Bundling and Capital Concentration Triple Barriers.
Theoretical and literature-based claim described by the authors; no empirical quantification of these barriers (e.g., number of embargoes, measures of rule bundling, capital concentration metrics) included in the excerpt.
Systematic skill differences cannot be captured by conventional measuring systems.
Comparative evaluation performed by the authors between conventional performance/skill measurement frameworks and patterns observed in their empirical dataset (5,000 job adverts and 2,000 salary records), leading to the conclusion that conventional systems miss systematic differences introduced by AI-enabled skills.
The emergence of ChatGPT in November 2022 disrupted practice in knowledge work and defied performance-measurement systems in human-exclusive task accomplishment under unprecedented comparability.
Author claim framed against timeline of ChatGPT release; contextualized by the study's broader empirical analysis (systematic analysis of 5,000 LinkedIn job adverts and 2,000 Indeed salary records from 2022–2024) used to support the narrative of disruption.
Organisations struggle to optimise human–AI collaboration in knowledge‑intensive decision‑making.
Statement based on a systematic synthesis of human–AI interaction and knowledge management literature presented in the paper; no primary empirical sample or dataset reported in the abstract.
Despite increased deployment, the field lacks a principled framework for answering when a team is helpful, how many agents to use, how team structure impacts performance, and whether a team is better than a single agent.
Authors' assessment of the literature and gaps; presented as a motivation for their work (no empirical count of missing frameworks given in excerpt).
Tasks that workers associate with a sense of agency or happiness may be disproportionately exposed to AI.
Empirical finding based on the paper's worker and developer surveys on 171 tasks, with LM scaling to 10,131 tasks; phrased cautiously in the paper as 'may be' disproportionately exposed.
There is a need for cross-jurisdictional regulatory standards to support deployment of ML-blockchain accounting systems.
Policy analysis and stakeholder feedback indicating regulatory fragmentation and the requirement for harmonized standards; asserted as a study finding. (Summary does not list consulted jurisdictions or regulatory bodies.)
Data privacy trade-offs are a significant challenge when combining ML and decentralized ledger technologies for accounting oversight.
Analytic discussion and evaluation of privacy implications arising from the hybrid architecture and use of decentralized ledgers with empirical datasets. (No specific privacy-attack tests or privacy metric values reported in the summary.)
The integration reveals scalability limitations as a critical challenge.
Findings from system evaluation and analysis that identified performance and scalability constraints when applying the hybrid solution to high-risk economic sectors. (No quantitative scalability metrics or testing conditions provided in the summary.)
Three skills degrade performance (up to -10%) due to version-mismatched guidance conflicting with project context.
Observed three skills with negative pass-rate changes up to -10% in the paired evaluation; authors attribute the degradation to guidance in the skills being mismatched to project versions/context.
Skill injection benefits are far more limited than rapid adoption suggests.
Aggregate evaluation results comparing agent performance with and without injected skills across the benchmark (49 skills, ~565 tasks) showing many skills yield no improvement and small average gains.
Evaluation frameworks remain predominantly model-centric, focusing on standalone AI performance rather than emergent collaborative outcomes.
Conceptual/literature critique presented in the paper motivating the new framework (review of prior evaluation practices; theoretical argument).
Aligned AI (trained to foster trust) can increase human trust but risks reinforcing suboptimal human behavior and lowering human-AI team performance.
Theoretical/ conceptual claim made in the paper (abstract); no specific empirical details provided in the excerpt.
Training AI to complement human strengths can decrease AI performance in areas where humans are strong, which can erode human trust and cause humans to ignore AI advice when it is most needed.
Argumentation and examples given in the paper (abstract); any empirical support referenced as part of the paper but sample sizes/details not provided in the excerpt.
Despite positive outcomes, challenges such as workforce displacement, ethical concerns, and limited access to AI technologies were identified as barriers to full adoption.
Study respondents reported barriers in the survey; descriptive statistics summarized the prevalence of workforce displacement concerns, ethical issues, and limited access to AI technologies as impediments to broader adoption.
There is a growing tension between relatively rigid education and training systems and the rapidly changing skill requirements of digitally driven labor markets.
Argument motivated and supported by comparative assessment of international practices and systemic analysis; descriptive/comparative evidence rather than quantified empirical testing.
Significant mediating barriers—low participation in AI training, uneven educational backgrounds, and demographic disparities related to gender and age—constrain widespread and effective AI adoption.
Mediation/conditional analyses reported in the study (based on survey items about training participation, education, gender, age) indicating these factors act as barriers to adoption and effectiveness.
Information saturation from AI output contributes to cognitive overload among employees.
Grounded in the paper's application of cognitive load theory to findings from surveys and organizational research; the excerpt gives no direct measures of information volume or its direct cognitive effects.
Extensive AI use correlates with measurable productivity losses.
Paper states this correlation is observed in organizational research and large-scale surveys; the excerpt lacks details on productivity measures, sample sizes, or statistical controls.
Extensive AI use correlates with increased decision fatigue.
Reported correlation based on the same cited large-scale surveys and organizational research; no methodological details or effect sizes provided in the excerpt.
Extensive AI use correlates with increased turnover intention among employees.
Paper reports correlations observed in recent large-scale surveys and organizational research; the excerpt does not provide correlation coefficients, sample sizes, or control variables.
AI-augmented work environments create cognitive overload through information saturation, relentless task-switching, and the demanding oversight of multiple AI agents.
Synthesis in the paper drawing on research on human-AI collaboration and cognitive load theory and citing organizational research; specific empirical methods or sample sizes not provided in the excerpt.
Employees using AI extensively report significant mental fatigue, dubbed 'AI brain fry.'
Stated in the paper as derived from recent large-scale surveys and organizational research; no specific sample size, survey instrument, or statistical details provided in the text excerpt.
Analyses of online job postings indicate significant declines in demand for highly automatable and entry-level roles.
Empirical studies using online job-posting data described in the paper (methods: job-posting frequency/trend analysis; sample size/timeframe not specified in the excerpt).
Since the public release of ChatGPT in November 2022, concerns regarding job displacement, wage reduction, and labor market restructuring have intensified.
Temporal observation in the paper referencing heightened public and policy concerns after ChatGPT's release; based on cited literature and discourse (no sample size given).
Low‑skill installation and maintenance jobs have increased, but wage levels and upward mobility for these jobs remain lower than those in high‑skill industries.
Finding reported from the literature review and cited reports/studies indicating growth in low‑skill installation/maintenance employment alongside comparative analyses of wages and career mobility; no specific datasets or sample sizes provided in the summary.
Job polarization is occurring in solar power plants as a result of automation or digital transformation and changes in required skill sets.
Synthesis from the systematic literature review and referenced reports/studies indicating links between automation/digitalization and occupational shifts in solar plants; specific studies and sample sizes not provided in the summary.
O SCF é expandido para uma camada de segunda ordem (SCF-E) que incorpora déficit de imaginação tecnocultural e governança simbólica, explicando por que a IA permanece em pilotos e não se converte em capacidade organizacional.
Extensão conceitual (segunda ordem) relatada no artigo; respaldada metodologicamente pela combinação QUAN→QUAL, incluindo etnografia orientada ao SCF (detalhes empíricos no corpo do artigo, não no resumo).
A literatura de adoção tecnológica (TAM, UTAUT, Difusão de Inovações) tende a tratar a resistência como variável comportamental genérica ou deficiência de 'treinamento', negligenciando dimensões simbólicas (ritos, identidades e poder), mecanismos cognitivos de ameaça (aversão à perda, sobrecarga e heurísticas) e seus efeitos econômicos.
Revisão bibliográfica e posicionamento teórico declarado no artigo comparando modelos consagrados com a perspectiva proposta; sem indicação de meta-análise ou contagem empírica no resumo.
A Fricção Psicoantropológica (SCF) é proposta e detalhada como um coeficiente mensurável do custo cultural e da resistência cognitiva que reduz a capacidade de pequenas e médias empresas (PMEs) de transformar iniciativas de Inteligência Artificial (IA) em geração de valor em escala.
Proposição teórica e operacionalização apresentada no artigo; desenho metodológico descrito como QUAN→QUAL incluindo construção de escala psicométrica e etnografia organizacional. O resumo não especifica tamanho de amostra para validação.
The paper highlights that urgent policy intervention is required to reestablish a balance between the benefits of AI and the ethical ramifications that arise from these technologies, with a particular emphasis on job displacement.
Author conclusion drawn from the stated literature-based analysis; the excerpt does not list the specific studies, empirical findings, or criteria used to reach this policy recommendation.
There has been an increase in the level of concern regarding the ethical implications arising from the automation of tasks and the subsequent job displacement due to AI.
Author statement based on a review of (unspecified) novel studies and existing literature; no empirical sample size, instrumentation, or quantitative measure of 'concern' reported in the provided text.
Over-reliance on data-driven insights without adequate human oversight can worsen market uncertainty.
Reported in the study's qualitative case studies and interpretive analysis as a potential negative consequence of improper AI/Big Data use (no quantified examples provided in the summary).
Algorithmic bias is a potential pitfall of using AI and Big Data that can exacerbate market uncertainty.
Identified as a risk in the paper's qualitative analysis and discussion of pitfalls (no incident counts or empirical quantification provided in the summary).
External pressures (e.g., pandemics, extreme weather, geopolitical conflicts) disproportionately affect peripheral suppliers in the construction supply chain network.
Mapping of challenge categories to network positions in the study showed external pressures concentrating at peripheral supplier nodes; based on interview reports and network coding (quantitative support not detailed in abstract).
Relationship and contract issues accumulate at high-centrality brokers, which exhibit a reported degree centrality of 0.818.
Result reported in the paper linking the thematic category (relationship/contract issues) to network nodes identified as high-centrality brokers; a numeric degree centrality value (0.818) is reported for these brokers. Underlying network constructed from thematic coding of interviews; sample size not provided in abstract.
Six main challenge categories (comprising 16 open codes) concentrate systematically at specific network positions.
Results reported: thematic grouping produced six challenge categories and 16 open codes, and these were mapped to positions in the network showing systematic concentration; underlying data derive from coded interviews and network mapping (sample size not given in abstract).
Alignment interventions (e.g., fine-tuning, instruction-following adjustments) can systematically reshape or obscure the cultural regularities learned during pretraining.
Analytical distinction drawn between base models and fine-tuned/aligned systems in the paper; claim based on conceptual analysis of how adaptation changes model behavior rather than on specific experimental results in the provided text.
The limitations of systems that prioritize academic pathways constrain workforce adaptability and inclusive labor market development.
Argument based on synthesis of empirical studies and secondary data connecting education pathway composition to workforce adaptability and inclusiveness (presented as a policy-relevant conclusion rather than a quantified causal estimate).
Skills mismatch in the labor market is structural and linked to education systems that prioritize academic pathways without adequate support for vocational and continuing training.
Integrated interpretation of comparative evidence and secondary data showing imbalances between academic and vocational provision and associated labor-market frictions (paper frames this as a structural conclusion; specific causal tests not described in the summary).
Expansion of intermediate vocational skills has been limited relative to the expansion of higher education.
Comparative evidence and secondary data showing smaller increases in intermediate vocational qualifications compared with higher education attainment (specific metrics/country coverage not provided in the summary).
The risk to the tax system is heightened by the federal government’s dependence on individual labor income even as economic value shifts toward mobile capital and AI ownership by large firms.
Analytical claim in the paper linking tax base dependence to shifts in economic value; no empirical measurement of 'mobile capital' or quantified shift included in the excerpt.
AI threatens to disrupt the tax system’s ability to fulfill its fundamental goals of raising revenue, redistributing income, and regulating taxpayer behavior.
Normative/policy argument made in the paper (no empirical testing or quantified projections provided in the excerpt).
These AI-driven outcomes will have far-reaching impacts on the federal tax system, which heavily relies on taxing individual labor income and payroll rather than capital or consumption.
Paper's policy analysis asserting the composition of federal tax reliance (no revenue breakdowns or statistical evidence included in the excerpt).
Even under optimistic projections, AI is expected to exacerbate wealth inequality because ownership and immense value are concentrated within a subset of Big Tech companies and AI startups.
Argumentative claim in the paper asserting concentration of ownership and value in certain firms; no empirical measures or firm-level data presented in the excerpt.
Some experts predict widespread job displacement due to AI.
Statement in the paper referencing expert predictions (no specific experts, studies, or sample sizes cited in the excerpt).