The Case for Expanding the Domain of Registered Reports: Confronting Academic Dishonesty and Declining Confidence in Science
Bibliographic record
Abstract
ABSTRACT Human Resource Management Journal is expanding the scope of registered reports to encompass all forms of empirical research in human resource management, regardless of data type or methodological approach. This editorial explains the rationale for this change. I begin by defining registered reports and tracing their origins. I then argue that academia's prevailing “publish or perish” culture has significantly eroded public confidence in science. The pressure to publish has fostered questionable research practices and diminished the overall quality of scholarship across disciplines, including management and business studies. I contend that registered reports—particularly in their expanded form—help guard against many of these practices and promote greater integrity in research. I conclude by offering practical guidance on how to construct a registered report.
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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 | 0.213 | 0.510 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.048 | 0.044 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.022 | 0.034 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".