Bibliographic record
Abstract
In this paper, we build on critical scholarship calling for a care revolution in geography by examining the comprehensive/qualifying exam (QE) process as a moment of intervention. In North America, aspiring doctoral candidates are typically expected to pass a QE before beginning their research. The way QEs are traditionally designed and implemented in the field of geography reinforces a particular canon and a certain way of being a geographer that excludes diverse knowledges. Doctoral students often experience preparing for and completing these exams as a specifically stressful and isolating period. Such an approach to QEs limits geography’s potential as a caring discipline. From our positions as a doctoral student and PhD supervisor, we use collaborative autoethnography to explore an alternative QE format. To better understand the potential of alternative QEs to support doctoral education in geography, we bring literature on QEs into conversation with feminist geography literature on care and academia, exploring the educational possibilities of practicing QEs in a way aligned with a caring academic praxis. Rather than being viewed as a rigorous and individualized test focused on creating ‘expert geographers,’ we suggest the discipline thinks about QEs as a process that encourages scholars to practically and relationally engage with diverse ways of knowing. Despite its potential, doing QEs differently within an uncaring university system can be challenging. It requires a great deal of relational care work to be done well. In conclusion, we consider how geographers might begin to practice QEs differently in order to imagine the discipline, and academia, otherwise.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 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".