Editorial: What are registered reports and why are they important to the future of human resource management research?
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.
Bibliographic record
Abstract
Abstract Human Resource Management Journal (HRMJ) is proud to offer a registered reports pathway to publication. A registered report is an innovative method of publication in which authors submit a research proposal for peer review prior to the collection and analysis of the data. At Stage 1, the Introduction, Literature Review, Theory, Hypotheses and a detailed Research Methods Protocol are peer reviewed. If the paper is accepted ‘in principle’ at this stage, the authors can then proceed to Stage 2, in which they collect and analyse the data according to the agreed protocol and write up the Results and Discussion sections of the study. The primary purpose of a registered report is to obviate the use of questionable research practices and insidious p‐hacking. For this reason, only deductive (theory‐testing) research is appropriate for this pathway to publication. Research published via a registered report is conceptually and methodologically robust, falsifiable and less likely to fall victim to irreproducibility. This article explains what registered reports are, why they are good for scientific discovery, how the human resource management (HRM) field can benefit from offering this pathway to publication and how HRM scholars can submit a registered report to HRMJ .
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it