Recognizing students’ expertise and insights in expanding forms of academic writing and publishing about learning and teaching
Bibliographic record
Abstract
Recognizing students as authors, reviewers, advisors, and editors in academic writing acknowledges students' expertise and the value of their unique perspectives.Integrating students into publishing practices, as we do in this journal, expands the sphere of student influence in the academy by structuring their expertise and insights into the development of knowledge about learning and teaching.In this editorial, we reflect on the role of students in IJSaP by sharing selected examples of how they are contributing to and creating scholarly conversations about students-as-partners work through their participation in the journal.This editorial is written by three faculty members of the editorial board and follows on from the editorial in the last issue that was written by five student members of the board (Ntem et al., 2020).Linking these affirmations of student contributions with a call for an expansion of writing genres to be inclusive of a greater number and a greater diversity of students (as well as staff/faculty/academics), we introduce a new section of the journal called Voices from the Field.Expanding IJSaP's commitment to inclusion, the focus of the inaugural version of this new section is on diversifying students-as-partners participants and practices.
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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 | 0.028 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".