An Examination of the Funding-Finding Relation in the Field of Management
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.366 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".