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An Examination of the Funding-Finding Relation in the Field of Management

2015· article· en· W2801910647 on OpenAlex
James G. Field, David Mihm, Ernest H. O’Boyle, Frank A. Bosco, Krista L. Uggerslev, Piers Steel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of CalgaryNorthern Alberta Institute of Technology
Fundersnot available
KeywordsBivariate analysisTypologyRelation (database)Agency (philosophy)Field (mathematics)PsychologyFunding AgencySocial psychologyPolitical sciencePublic relationsSociologySocial scienceStatisticsMathematicsComputer scienceData mining

Abstract

fetched live from OpenAlex

Research in the field of medicine has indicated that the presence of research funding can lead to conflicts of interest, resulting in pressures to produce results that are palatable to the funding agency. Using a funding source typology, we examine if similar conflicts of interest exist in the field of management by analyzing over 156,000 effect sizes reported in Journal of Applied Psychology and Personnel Psychology from 1980-2010. In addition, we investigate the potential moderating impacts of funding type, bivariate relation type, as well as their interaction on the funding-finding relation. Results indicate that effect size magnitude is not impacted by the presence or source of research funding across broad bivariate relation type. However, funded studies have a higher proportion of statistically significant findings (69% of comparisons) and were also characterized by larger sample sizes (75% of comparisons). The pattern of results supports a methodological enhancement explanation for the funding-finding relation rather than a questionable research practices-based explanation. We conclude with recommendations for future research in this area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0140.055
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.496
GPT teacher head0.541
Teacher spread0.045 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it