Insider Experiences of The Qualitative Report’s Reviewing Process
Bibliographic record
Abstract
We (Pamela, Tom, and Jenn) wanted to give you our insiders’ experiences of the manuscript submission and reviewing process at The Qualitative Report (TQR). Respectively, we are a researcher-author, an instructor-reviewer, and a student-reviewer who were involved in the reviewing process that resulted in the publication of Pamela’s TQR article: On Doctoral Student Development: Exploring Faculty Mentoring in the Shaping of African American Doctoral Student Success (Felder, in press). In this brief article, we will adopt a somewhat conversational approach to relating our individual and collective experiences. How we came to work together, what that work entailed, and our experiences of that collaborative work will be our focus. In short, we offer our insiders’ sense of (and reflections on) what happens to a manuscript from the time of its submission to the time of its publication at TQR.
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.
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 | 0.228 | 0.399 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.032 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".