Evidence (16496 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Under capability-superset accounting on the curated gold competitive record, agent A recovers only 0.25, agent B recovers 0.38, while agent C recovers 0.96 (overall).
Capability-superset accounting comparison of fraction of a curated gold competitive record recovered by each agent on the benchmark.
The prominence of machine learning, Internet of Things (IoT), and cybersecurity varies depending on organisational context and role requirements within the wind sector.
Paper reports variation across data sources and organisational contexts based on interviews, surveys, and job-posting patterns; no subgroup sample sizes or statistical tests reported in summary.
The Recuse Signal behaves as a cooperative rather than absolute signal: an explicit operator-authorization framing flips the most capable model to proceed, while other agents continue to defer to the on-host policy.
Observation from the pilot experiment (SSH) with multiple deployed agents (GPT-4o, GPT-4o-mini, Claude Code); experiment included alternate framing where operator authorization was explicit.
Data contamination (training-data overlap) complicates interpretation of the models' performance.
Author notes the possibility that models' training data may have contained the target papers or related material, making results ambiguous.
AI is best understood as a real technological revolution with localized bubble dynamics rather than as either a pure speculative mania or a bubble-free productivity miracle (central conclusion).
Synthesis of the paper's review and diagnostic findings combining asset-pricing theory, empirical evidence on fundamentals, and bubble-detection diagnostics.
Current evidence shows both genuine fundamentals and bubble-like fragilities in AI valuations.
Synthesis of reviewed empirical findings in the paper: realized revenue growth, enterprise adoption, productivity evidence (supporting fundamentals) and faster capex vs monetization, concentrated private-market valuations, and narrative-driven investor behavior (supporting fragilities).
The same practice input carries opposite signs depending on whether the environment screens for it.
Synthesis of empirical patterns: in unscreened CF environment AI-style practice predicts smaller rating gains (for non-affiliated users) while in screened ICPC environment it predicts higher non-AI-aided scores.
In open Codeforces contests a stronger AI-style signature predicts smaller rating gains for users with no ICPC/IOI affiliation, but not for those who qualified for the AI-prohibited contests.
Comparative empirical analysis of CF contest rating gains by users' affiliation (ICPC/IOI qualification status) and individual AI-style signature strength; methods likely regression/heterogeneity analysis—sample sizes not reported in abstract.
The paper provides a consolidated, theory‑driven synthesis of the mechanisms through which AI‑mediated platforms simultaneously create opportunities and reproduce disadvantage for women.
Originality/value statement in the paper describing its contribution as a consolidated, theory‑driven synthesis and actionable insights for researchers, policymakers, and platform designers.
National AI development can be interpreted as a controlled balance between information injection and entropy dissipation.
Theoretical mapping using HCLM; paper presents this dynamical framing and definitions of the two processes; no empirical sample.
Advanced economies have integrated AI technologies at scale, while emerging economies such as Algeria face structural and institutional challenges that limit the potential impact of AI on productivity growth.
Asserted in the paper with supporting literature citations (Agrawal et al., 2019; Acemoglu & Restrepo, 2020) and comparative use of World Bank and Oxford Insights indices; no specific sample-size based causal estimate provided.
A safety monitor condition reduces sabotage success, but 56% of participants still accept the malicious code, ignoring its warnings.
Experimental manipulation: one condition included a safety monitor. Authors report that the monitor reduced sabotage success (no absolute reduction magnitude reported here) and that 56% of participants in that context accepted malicious code despite warnings.
These examples show an important shift in the governance of wealth chains – the creation of new forms of infrastructural power through which algorithmic models may become central nodes in tax governance.
Synthesis/interpretive conclusion in the abstract that the illustrative examples imply a governance shift and new infrastructural power; presented as interpretive argument rather than empirically demonstrated in the abstract.
This signals a transformation of the assumed information asymmetries between suppliers, clients, and regulators that sits at the heart of the Global Wealth Chains framework.
Conceptual claim in the abstract linking technological change to shifts in information asymmetries within the Global Wealth Chains framework; presented as interpretive argument rather than supported by reported empirical data in the abstract.
A key development is a move away from deliberate opacity for secrecy purposes into systems that search for the optimal exploitation of legal affordances.
Analytic/interpretive claim made in the abstract about a shift in practices; presented as an argument based on the authors' reflection and examples rather than empirical measurement in the abstract.
There is significant cross-national, cross-industry, and cross-regional heterogeneity in AI's impact.
Conclusion from the systematic literature review indicating variation across countries, industries and regions in the effects reported by prior studies.
Research has shown that artificial intelligence is primarily driven by substitution effects in the short term, but will generate complementary and creative effects in the long term.
Synthesis claim from the literature review; the paper reports this as an aggregate finding from prior studies (no single-study sample size provided).
The paper analyzes the direct impact of artificial intelligence on employment structure, occupational tasks, and skill demand, as well as its indirect effects on job mobility, cross-border and industry differences, and policy interventions.
Descriptive claim of scope drawn from the systematic literature review conducted by the authors; no single empirical sample reported.
The rapid development of artificial intelligence is profoundly reshaping the global labor market landscape.
Statement in paper based on a systematic literature review synthesizing prior studies; no single empirical sample reported.
Analysis of recent benchmark evidence including SWE-bench Verified, EvoClaw, and LangChain's multi-agent coordination studies demonstrates both the transformative potential of the agentic paradigm and its current limitations.
Empirical/benchmark analysis referencing SWE-bench Verified, EvoClaw, and LangChain multi-agent studies as sources of evidence; the paper analyzes these benchmarks qualitatively or comparatively (specific sample sizes and quantitative effect sizes not stated in the abstract).
By redefining discoverability metrics and authority signals, LLM-integrated search ecosystems are reshaping digital marketing economics.
Argumentative claim in the paper linking shifts in discoverability and authority to broader digital marketing economic effects; presented as conceptual synthesis without quantitative evidence in the excerpt.
Visibility in LLM-integrated search is shifting from click-through optimization to 'Answer Inclusion Optimization' (AIO), where visibility depends on whether content is selected, synthesized, and cited within AI-generated responses rather than on SERP ranking alone.
Conceptual proposition and terminology introduced by the authors (AIO); presented as a reframing of visibility metrics rather than backed by quantified experiments in the excerpt.
The rapid integration of large language models (LLMs) into search engines and conversational AI platforms is fundamentally transforming the landscape of search engine optimization (SEO).
Statement in paper's introduction asserting observed integration of LLMs into search engines and conversational platforms; based on conceptual analysis and literature synthesis (no empirical sample or quantified measurement provided).
Collective practices that emerge in response (from shared prompt strategies to jailbreaking techniques) represent vernacular knowledge formations that, while often exhibiting magical thinking, contain resources for 'revolutionary prompting' and the transformation of individual prompt anxiety into collective political critique.
Qualitative/interpretive claim based on observed user practices and collective responses to LLM behaviour; no systematic survey or sample sizes reported in the abstract.
Overall, STARA technologies can both enhance skill development, thriving and career opportunities and concurrently produce identity threats, pressures, and contextual complexities that shape long-term career trajectories—requiring integrated organisational and labour-market perspectives to design supportive approaches.
Editorial synthesis and summary of contributions in the special issue; draws on multiple cited empirical and conceptual studies included in the issue and prior literature.
In the platform economy, performance and career success are increasingly captured through alternative, often real-time metrics, diverging from traditional indicators and raising challenges for integrating conventional and non-traditional measures of career outcomes.
Synthesis of literature on platform work and algorithmic management cited in the editorial (multiple references to platform economy research and contributions to the special issue).
Algorithmic systems for productivity and performance monitoring generate efficiencies but also create new pressures in technology-mediated work environments, including the tracking of employees’ emotional and physiological responses at work and during non-work time.
Literature synthesis and citations (e.g. Giermindl et al., 2022; McCartney and Fu, 2022; Norlander et al., 2021; Downie et al., 2025).
AI usage at work can simultaneously enhance employees' thriving and induce identity threat; employees’ learning and performance goal orientations drive career growth in this context (Yuan et al., 2026, in this special issue).
Reported empirical finding from a paper in the special issue (Yuan et al., 2026) cited in the editorial.
Significant advancements in smart technology, AI, robotics and algorithms (STARA) are changing how organisations design and implement work for the current and future workforce.
Statement in the editorial supported by references to prior literature and reviews (e.g. Brougham and Haar, 2018; Raisch and Krakowski, 2021; Tang et al., 2023; Ulfert et al., 2024; Yam et al., 2023). This paper is an editorial/literature-synthesis rather than a primary empirical study.
Aggregate AI metrics (the composite AI Vibrancy Score) obscure heterogeneous pillar-level effects on tourism’s economic contribution.
Comparison of null result for the aggregate AI Vibrancy Score with significant positive effects for specific pillars (R&D, Policy and Governance, lagged Talent) in the same fixed-effects analyses on 33 countries (2017–2023).
Grounding the concept of defensive AI governance in organisation-level evidence from the Global South contributes to debates on platform power, journalistic agency, and AI governance in journalism.
Theoretical/interpretive claim based on the study's case of Al-Masry Al-Youm and its empirical insights; presented as a contribution to scholarly debates. Sample size not reported in the excerpt.
The authors introduce the concept of 'defensive AI governance' to describe how AI adoption is managed through organisational practices of limitation, supervision, and infrastructural self-protection.
Conceptual contribution grounded in organisation-level qualitative evidence from interviews and analysis of Al-Masry Al-Youm's practices; the concept is derived from the study's empirical findings. Sample size not reported in the excerpt.
The newsroom adopts, adapts, and governs AI across data journalism, fact-checking, and generative applications.
Empirical observations and interview data from Al-Masry Al-Youm detailing specific domains of AI integration (data journalism, fact-checking, generative tools). Sample size not reported in the excerpt.
AI functions as a conditional capability amplifier, expanding agency while producing uneven inclusion shaped by disparities in connectivity, skills, and infrastructure.
Analytical synthesis and illustrative empirical evidence from interviews showing differential effects tied to connectivity, skills, and infrastructure.
Human and algorithmic actors jointly influence strategic outcomes, motivating the concept of 'hybrid upper echelons' in which executive influence increasingly shifts from making decisions to configuring and governing AI-enabled decision processes.
Theoretical contribution based on integration of management and IS literature in the concept-centric review; proposition of a new conceptual framework ('hybrid upper echelons') rather than primary empirical validation.
AI reconfigures UET through discretion reconfiguration: AI enables delegation and embedding of decision authority, redistributing managerial discretion.
Concept-centric literature review synthesizing studies on delegation/automation of decision authority and managerial discretion (no primary empirical sample reported).
AI reconfigures UET through evaluation reconfiguration: AI partially substitutes human judgment with algorithmic decision logic and thereby shapes how alternatives are evaluated.
Conceptual synthesis from the literature review integrating findings from management and IS studies on algorithmic decision logic and judgment substitution (no primary empirical sample reported).
AI reconfigures upper echelons theory (UET) through cognition reconfiguration: AI mediates information and attention, expanding analytical capacity while introducing new constraints on executive cognition.
Synthesis of management and IS research in a concept-centric literature review; conceptual argument drawing on prior studies about information mediation and attention (no primary empirical sample reported).
An explicit thinking mode raises rank-order correlation without moving accuracy.
Empirical comparison of reasoning modes showing increased rank-order correlation (e.g., Spearman/Fisher-z) when explicit 'thinking' mode is used, with no significant change in accuracy.
Most published twins are either coarse persona bots conditioned on a few demographic questions or detailed individual-level twins built on purpose-collected surveys and interview transcripts.
Author's literature summary / positioning statement in paper (qualitative assessment of existing published twins).
AI-mediated financial decisions are reflexive: they reshape organizational workflows, prices, liquidity, credit allocation, and the future data on which subsequent decisions rely.
Conceptual argument supported by literature across finance and related fields (review-level synthesis; no single empirical sample size reported).
Human–AI complementarity in finance is conditional rather than automatic, depending on task structure, private information, feedback quality, incentives, explanation design, and governance.
Synthesis of literature from finance, management, HCI, and AI showing moderating factors for complementarity (conceptual integration; no unified empirical sample size reported).
Overall, the digital economy brings both opportunities (raising incomes overall) and challenges (contributing to greater inequality).
Synthesis of empirical findings from the two-way fixed effects panel (31 provinces, 2011–2021) and robustness checks indicating positive average income effects alongside heterogeneous effects that widen disparities.
Non-state-owned enterprises (non-SOEs) benefit more from the digital economy than state-owned enterprises (SOEs), attributed to their greater flexibility and adaptability.
Ownership-type heterogeneity tests in the paper's two-way fixed effects panel (31 provinces, 2011–2021) showing larger estimated income/benefit effects for non-SOEs than for SOEs; interpretation links this to adaptability.
Industry-wise, sectors with higher levels of digitalization (e.g., mining, finance, energy) show stronger income effects, while traditional sectors (e.g., agriculture, public services) show limited impact.
Industry-level heterogeneity analysis in the two-way fixed effects panel using provincial data (2011–2021), reporting larger estimated effects for high-digital sectors and small or null effects for traditional sectors.
Regionally, eastern provinces experience greater income gains from digital development than central and western provinces.
Regional heterogeneity results from the paper's two-way fixed effects panel (31 provinces, 2011–2021) comparing estimated effects across eastern, central, and western regions.
The paper's contribution includes an estimand distinction, an inspectable ABM/RL mechanism, and a reproducible artifact demonstrating that transparent behavioral assumptions are sufficient to generate gaming-like boundary dynamics without implying that computable regulation is inherently undesirable.
Author-stated contributions in the abstract describing methodological and reproducibility outputs (estimand distinction, inspectable model, reproducible artifact).
AI-flagged complaints are geographically unevenly distributed.
Geographic analysis of AI-flagged complaint shares across jurisdictions using case metadata; authors report uneven distribution.
Overall, complementarity is attainable in multi-agent regression but obstructed in classification under natural conditions on local aggregation and loss functions.
Synthesis of the paper's proved positive results for regression and negative impossibility results for classification within the tree-based HAI framework (theoretical proofs; no empirical sample).
In regression under squared loss, complementarity is equivalent to Euclidean distance minimization from the ground-truth vector.
Analytic equivalence proved in the paper for the tree-based model under squared loss (mathematical derivation; no empirical sample).