Diversifying students-as-partners participants and practices
Bibliographic record
Abstract
In this issue of IJSaP, we are piloting this new "Voices from the Field" section with a collection of contributions that highlight the importance of increasing diversity among students-as-partners participants and diversifying students-as-partners practices. Two IJSaP coeditors, Alison Cook-Sather (faculty co-editor) and Sarah Slates (student co-editor), assumed leadership for this pilot section. We have taken an approach to crafting it that borrows from some established practices in publishing in general and in partnership spaces in particular and that experiments with some new ones. The goal of this new section is to create a space to share a diversity of emerging ideas, opinions, and perspectives on important questions about partnership work. More specifically, we aim to support the voices of those who might not normally be represented within traditional forms of academic publishing and/or who do not have time for, or interest in, working through the peer-review process but who have something to say. We juxtapose authors' perspectives under categories that emerged from the contributions, and we present them with minimal editing, interpretation, or analysis so that the voices can speak for themselves, to one another, and with readers.
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 | 0.125 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".