Uncovering hidden curriculums of heteronormativity: a critical literature review of doctoral student socialization
Bibliographic record
Abstract
Doctoral student retention and successful completion, among other significant aspects of the doctoral student experience, have been theorized primarily through the socialization framework. This body of literature has been critiqued over the past decade for its assumptions regarding the uniformity of the doctoral student population, and subsequently expanded to question the accessibility and equitability of socialization processes for students from marginalized racialized, gendered, and socioeconomic backgrounds. A discussion of queer doctoral students, however, remains noticeably absent. In this paper, I offer a critique of the doctoral student socialization literature that addresses the exclusion of the queer doctoral student. By putting traditional literature on doctoral student socialization and its recent critiques in conversation with literature on queer faculty in academia and queer interventions into related higher education literatures, I highlight the existence of a hidden curriculum of heteronormativity within the doctoral student socialization process. I argue that this process reproduces the marginalization of queerness. This paper therefore addresses a significant gap in the doctoral student socialization literature by centering the missing figure of the queer doctoral student, and expanding our understandings of differential access to the socialization process and to the successful completion of doctoral studies.
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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 | 0.059 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".