Evidence (4892 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 |
Org Design
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The empirical analysis used archival microdata from 770 large Spanish firms and employed staged OLS regression models.
Statement of data source and method in the paper's abstract.
The complementarity between AI deployment depth and breadth offers a configurational explanation for the AI productivity paradox.
Theoretical interpretation plus empirical finding of a positive interaction between depth and breadth in staged OLS analyses of archival microdata from 770 large Spanish firms.
AI capability can be conceptualized as two-dimensional: AI deployment depth (technological variety of AI implementations) and AI deployment breadth (organizational scope of AI diffusion).
Theoretical framing drawing on Resource-Based Theory and organizational search theory; conceptual argument presented in the paper.
Those preliminary experiments do not establish behavior preservation, scaling economics, or verified-change cost.
Authors' explicit limitation statement following the preliminary QLoRA experiments.
The study's findings relate to internal AI capability development and may not fully capture firms' reliance on external AI solutions.
Limitation/qualifying statement in abstract about scope—focus on internal capability development rather than external solutions.
The analysis draws on a novel dataset (Babina et al., 2024) combining resume and job posting data for U.S. firms.
Statement in abstract describing data source; dataset is cited as Babina et al. (2024) combining resumes and job postings for U.S. firms.
From 2024 to 2026, more than 130 articles were submitted to this Special Issue (SI), and only 18 papers were accepted after rigorous peer review.
Editorial report in the paper describing CFP submissions and acceptance counts.
We conduct a qualitative study on a representative sample of 306 non-merged pull requests created or co-authored by the agents mentioned earlier, followed by a quantitative analysis of the reasons for rejection.
Authors' reported methods: qualitative study of a sample of 306 non-merged PRs and subsequent quantitative analysis.
In a production switchback experiment, the offline-trained policy reduces courier-side time costs without degrading customer-facing delivery quality.
Empirical claim supported by production switchback experiment described in the paper; asserts no degradation in customer-facing delivery quality concurrent with courier-side time improvements (no numerical metrics or sample sizes provided in excerpt).
After screening, 35 studies were included in the thematic synthesis and supplemented by official regulatory and industry documents.
Review screening result reported in the paper: number of included studies = 35; supplementation by regulatory and industry documents stated.
A structured search protocol was designed for Scopus, Web of Science, PubMed, IEEE Xplore, and Google Scholar covering January 2016 to May 2026, English-language records only.
Methods statement in the review describing the databases, date range, and language restriction used for the systematic search.
The implementation literature on AI for pharmacy inventory and pharmaceutical supply chains remains dispersed across pharmacy operations, operations research, health informatics, and supply chain analytics.
The review's thematic synthesis of the searched literature (review methods described below) identified studies across these disciplinary areas.
Specification, reference implementation, conformance suite, and worked examples are available at: https://github.com/BrightbeamAI/chap
Claim of artifact availability hosted on GitHub (URL provided) as part of the paper's resources.
Two protocol standards address adjacent concerns: MCP standardises agent access to tools and data, and A2A standardises agent-to-agent interoperability.
Factual claim referencing existing standards (MCP and A2A) and their scopes; no citations or supporting documentation included in the provided excerpt.
Production deployments are no longer one human supervising one model; they are multi-human, multi-agent collaborations that cross teams, time zones, and trust boundaries.
Stated as a general characterization of modern production deployments; no quantitative data or case counts provided in the excerpt.
The findings provide empirical insights for managing employee wellbeing and refining human resource strategies during organizational digital transformation.
Authors' stated implications in the discussion, based on the reported empirical associations and moderation results from the survey of 411 employees.
The study draws on the Conservation of Resources Theory and the Cognitive Appraisal Theory of Stress to explain how AI application influences employees' job insecurity via resource gain and resource threat mechanisms.
Theoretical framing stated in the introduction and discussion explaining the mechanisms (resource gain vs. resource threat) underlying the observed U-shaped association.
Data were collected via mixed online and offline questionnaires: 453 questionnaires were distributed (242 online, 211 offline); 449 were returned (242 online, 207 offline); following validity screening, 411 valid questionnaires were retained (219 online, 192 offline), yielding an effective response rate of 90.73%.
Reported survey administration and response counts provided in the methods section of the paper.
Devil's Advocate (DA) is an AI assistant that critiques the human's initial ideas, whereas Dialectical Inquiry (DI) provides alternatives and synthesizes a resolution.
Conceptual/definitional claim in the paper describing the operationalization of DA and DI for the experiments.
This research empirically compares DA and DI in AI contexts.
Paper reports experimental comparison between AI behaviors implementing Devil's Advocate (DA) and Dialectical Inquiry (DI) across the studies.
Both studies examine benefit (information elaboration) and cost (cognitive load) pathways when AI supports SDM.
Paper explicitly frames both studies to measure information elaboration as a benefit pathway and cognitive load as a cost pathway; stated measurement plan in methods.
Study 2 tests mind-shaping interventions through user strategy training.
Study design described in the paper: a second experiment (Study 2) manipulating user strategy training (mind-shaping) to evaluate effects on SDM processes and outcomes.
Study 1 tests tool-shaping interventions by comparing three AI bot prototype conditions (Information-only, DA, DI) against a control treatment.
Study design described in the paper: randomized/controlled experiment (Study 1) with four conditions (three AI prototype conditions plus control).
We evaluate the system on operator feedback and a question set collected from production usage, graded by human and automated panels.
Paper's stated evaluation methodology: operator feedback + production question set, graded by humans and automated panels.
Traditional software and agentic systems are distinct: in traditional software code is the carrier of decision logic, whereas in agentic systems code is ephemeral tooling used by an LLM-driven reasoning loop.
Formalization and conceptual definitions developed in the paper (first-principles formal distinction; no empirical sample size reported).
For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
Historical/descriptive claim presented in the paper's framing and literature review; citation of longstanding software engineering practices (qualitative, no empirical sample size reported).
We implement a two-stage processing architecture separating document-level extraction (Stage 1) from claim-level synthesis (Stage 2).
Implementation description in paper: architecture design and pipeline stages described by the authors.
Verified word-count analysis of the Executive Order shows the word 'security' appears 17× and the word 'cyber' appears 14×, while there are zero mentions of 'labor', 'education', 'culture', 'fairness', 'transparency', 'attribution', 'provenance', 'meaning', or 'commons'.
Automated/count-based analysis of the EO text (single-document word-count reported in the paper).
These are mechanism-oriented synthetic results, not estimates of real firm behavior in a jurisdiction or industry.
Explicit qualification in the abstract stating the scope and limits of inference (paper text).
The study uses a synthetic agent-based reinforcement-learning simulation that separates actual conduct near a legal threshold from proximity in the computable enforcement signal.
Methodological description in abstract: ABM/RL simulation with explicit separation of conduct vs. computable signal; run counts reported (150 seed-level scenario runs, 378 computability-sweep runs, 288 Latin-hypercube runs) and a 2,880,000-row firm-period panel.
Ordinary adaptive updates do not reliably reduce boundary search.
ABM/RL simulation experiments reported in the paper (multiple runs and the firm-period panel); qualitative comparative statement from simulation outputs.
The distinction matters: debt is a stock of design and governance liability, while the tax is a flow of operating cost that arises because stochastic agents act through tools and workflows.
Conceptual argument in the paper articulating difference between two defined concepts (Agentic Technical Debt vs Stochastic Tax); no empirical demonstration.
Stochastic Tax is the recurring operating burden of keeping probabilistic agent behavior within acceptable bounds.
Paper provides a formal definition / conceptual framing of 'Stochastic Tax'; stated as an operational concept (no empirical quantification provided).
Agentic Technical Debt is the accumulated liability created when prompts, memory, tool schemas, orchestration graphs, control policies, and observability routines are patched together faster than they can be validated, standardized, and governed.
Paper provides a formal definition / conceptual framing of 'Agentic Technical Debt'; presented as a definitional contribution rather than an empirically measured quantity.
Agentic AI systems reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback.
Descriptive/definitional statement in the paper; presented as characteristics of agentic systems rather than supported by empirical measurement.
Agentic AI systems are increasingly being explored as production infrastructure.
Stated as an observation in the paper's introduction/abstract; no empirical data, sample, or formal measurement provided (conceptual/observational claim).
The paper evaluates the proposed architecture using the outcome metric 'time-to-insight'.
Methodological statement in the paper listing evaluation metrics.
The paper evaluates the proposed architecture using the outcome metric 'time-to-find'.
Methodological statement in the paper listing evaluation metrics.
The paper evaluates the proposed architecture using the outcome metric 'data product adoption'.
Methodological statement in the paper listing evaluation metrics.
In the first acquisition the acquirer pursued a disruptive 'rip-and-replace' strategy for the target’s proprietary ERP system.
Empirical observation from the paper's comparative case study of two consecutive acquisitions of the same digital target (qualitative case evidence).
We identify four archetypes (data orchestrators, aggregators, niche specialists, and cloud orchestrators).
Paper states it develops a taxonomy and explicitly lists four archetypes; based on the taxonomy development and conceptual classification reported in the paper (no sample size or quantitative empirical test reported in abstract).
We examined how different degrees of embodiment affect team performance and conversational dynamics in a real-life escape room; teams were composed of either three humans or two humans and an artificial agent (a Box, an Avatar, or a hyper-realistic humanoid).
Experimental field study reported in the paper: a real-life escape room experiment comparing team compositions (3 humans vs. 2 humans + agent of three embodiment types). Sample size not reported in the provided text.
To the best of the authors' knowledge, no prior study has examined the psychological mechanism through which algorithmic management shapes employee voice and silence behaviour outside of gig economy and platform work contexts.
Author claim based on literature review (stated gap in existing research).
Estimation accuracy depended only weakly on message volume, indicating that more text alone does not guarantee better inference.
Analysis reported in the paper examining the relationship between message volume and estimation accuracy; described as a weak dependency.
Regression models and moderation analyses were performed in R to examine associations between governance exposure, AI maturity, and adaptation intensity.
Methods statement: 'Regression models and moderation analyses were performed in R (R Computing, Austria) to examine associations between governance exposure, AI maturity, and adaptation intensity.'
Path-specific composite indices for bifurcation, modularity, ethical signaling, and compartmentalization were quantified using validated scales.
Methods description in the paper: 'Path-specific composite indices ... were quantified using validated scales.'
The study coded 500 adaptation events.
Explicit statement: 'and 500 coded adaptation events.'
The qualitative dataset included 48 executive and technical informants.
Explicit statement: 'including 48 executive and technical informants'.
The study uses a comparative multi-case dataset of 12 multinational firms (4 tri-jurisdictional, 4 Atlantic, 4 China-primary).
Explicit dataset description in the paper: 'A comparative multi-case dataset of 12 multinational firms (4 tri-jurisdictional, 4 Atlantic, 4 China-primary) was analyzed.'
The model introduces the 'Sciencepreneur' as the central human archetype in agentic R&D.
Conceptual/design claim within the HARMONY artifact presented in the paper.