Resurrecting a dead manuscript: Tales from the crypt
Bibliographic record
Abstract
Several years ago, I (Carrie) offered my undergraduate research team the opportunity to complete an experiment, but not the typical kind undergraduates usually participate in.I proposed that we work together to update and revise a previously rejected manuscript of mine.Over several years (thanks COVID-19 pandemic), we worked together in person and online to rewrite and refine the piece.We had the paper accepted for publication in the Journal of Clinical Sport Psychology, and now we are thrilled to have the opportunity to reflect on our collective and individual experiences with this project.Active involvement in a research project can greatly benefit students by linking conducting research and teaching (Healey, 2005).As indicated by the three students' perspectives, their active involvement in the research, publication, and dissemination processes has greatly furthered their academic ability and their respective paths within psychology.The aim of our essay is to provide an authentic account of the experiences and outcomes of the authors.
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 | 0.025 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".