Naming is Power
Bibliographic record
Abstract
Citing is a political act. It is a practice that can work both sides of the same coin: it can give voice, and it can silence. Through this research, we call for those contributing to the scholarship of teaching and learning (SoTL) to attend to this duality explicitly and intentionally. In this multidisciplinary field, SoTL knowledge-producers bring the citation norms of their home disciplines, a habit that calls for interrogation and negotiation of the citation practices used in this shared space. The aim of our study was to gather data about how citation is practiced within the SoTL community: who we cite, how we cite, and what values, priorities, and politics are conveyed in these practices. We were also interested in whether any self-selected categories of identity (e.g., gender, career stage) related to self-described citation practices and priorities. Findings suggest several statistically significant relationships did emerge, which we identify as important avenues for further research and writing. We conclude with 10 principles of citation practices in SoTL.
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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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.051 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".