Corridor Talk: Canadian Feminist Scholars Share Stories of Research Partnerships
Bibliographic record
Abstract
Corridor Talk contains contributions from feminist scholars from across Canada from a variety of disciplinary backgrounds. When the anthropologist Paul Rainbow coined the term, 'corridor talk,' he used it to refer to information that was relegated to side chats with colleagues, information that was not to be included in field notes, manuscripts or journal articles. These were the unimportant details or 'gossip' concerning a person's research, although he noted that a person's reputation often hinged on such discussions. Most feminist scholars, like many working within the realm of qualitative methodology, have for many years, rejected this discourse of 'unimportant details' and have chosen instead to document experiences and struggles during the research process as a way of exploring such issues as: whose interests are served by the research, what is the purpose(s) of the research, what are the goals of the research? In this book, graduate students, sessionals, independent scholars, community members, as well as established scholars, have an opportunity to share their experiences with the reader about doing feminist research, including the pitfalls, the benefit of hindsight, the 'what ifs' and the 'ah ha' moments. By sharing stories about the emotional struggles and methodological dilemmas that occur in the process of doing feminist research, the authors provide researchers, both seasoned and new, an invaluable inside look at conducting feminist research.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.113 | 0.044 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.008 | 0.028 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 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".