Doctoral writing and the politics of citation use
Bibliographic record
Abstract
Conventions shape scholarly writing and citations practices are one set of conventions that dominate how and what we write. Yet, many of these practices naturalize exclusion and discrimination in a way that becomes normalized and, consequently, invisible. For doctoral students, learning the conventions of citing is part of developing an identity around scholarship, research and writing. In this paper, we examine our own experiences of the politics of citations to understand our socialization processes and resistances. We use an autoethnographic narrative approach to frame this qualitative study. Our findings show how citation use abounds with the contradictions and paradoxes in our doctoral writing journeys where the pressure to succeed can compromise identity-building as ethical scholars. Each of us has many needs and multiple positionalities and resisting the naturalizing grammar of citations can be complicated. Yet, once aware of the politics of citations, one cannot go back to being unaware.
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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 | 0.043 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.022 | 0.050 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".