A Protocol for Co-Authored Academic Writing: The “Draft-in-a-Day”
Bibliographic record
Abstract
The iterative process of writing a co-authored manuscript in sport and exercise psychology may take several months to complete. Draft-in-a-day is an alternative group-based approach to writing that draws on concepts from sport and exercise psychology to efficiently write the first draft of a manuscript, while providing rich opportunities for trainees to develop their writing skills. The purpose of this paper was to explore the usefulness and acceptability of draft-in-a-day by examining individuals’ experiences using the draft-in-a-day protocol. Twelve participants (4 professors, 8 trainees) who had used the draft-in-a-day protocol completed an online questionnaire about their experiences. Participant responses were inductively content analyzed. Participants were receptive to the draft-in-a-day method of writing, reported being very likely to use it in the future (M = 4.9, SD = 0.28; scale 1-5), and provided suggestions for improvement. This early stage research provides a framework for efficient group-based writing in sport and exercise psychology.
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 | 0.113 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.036 |
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".