Improving the Quality of Mixed Research Reports in the Field of Human Resource Development and Beyond: A Call for Rigor as an Ethical Practice
Bibliographic record
Abstract
Since 2000, only 13% of the total number of empirical research articles (n = 230) published inHuman Resource Development Quarterly (HRDQ)have represented mixed research studies. Plausible explanations for why theHRDQprevalence rate is not more than 13% include the possibility that a high proportion of mixed research studies that are being submitted toHDRQare not of sufficient quality to be accepted. Thus, in this editorial, we provide evidence‐based guidelines for conducting and reporting mixed research that are framed around Collins, Onwuegbuzie, and Sutton's (2006) 13‐step model of the mixed research process. Further, we divide our reporting standards into four general areas—research formulation, research planning, research implementation, and research dissemination—that we itemize via a taxonomy that contains more than 60 elements.
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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 | 0.875 | 0.947 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.014 | 0.070 |
| Scholarly communication | 0.051 | 0.033 |
| Open science | 0.017 | 0.020 |
| Research integrity | 0.033 | 0.043 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".