The Vulnerable Researcher: Some Unanticipated Challenges of Doctoral Fieldwork
Bibliographic record
Abstract
This paper draws explicitly on the field experiences of two doctoral researchers in geography to elucidate some of the challenges and issues related to researcher vulnerability that are especially acute for graduate students. In spite of significant differences in context, both researchers experienced an unanticipated degree of professional vulnerability during their doctoral fieldwork that warrants further exploration, including a theoretical interrogation of the complex (and shifting) terrain of power relations within qualitative research projects. This paper addresses the lacuna in the qualitative methodological research literature on the topic of researcher vulnerability (in contrast to the well-developed discussion of participant vulnerability). Throughout, the authors suggest possible strategies for mitigating researcher vulnerability while protecting the overall integrity of the research process.
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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 | 0.161 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.040 | 0.063 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.009 | 0.012 |
| 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".