Cohorts and coalition building for First Nations graduate students
Bibliographic record
Abstract
Cohorts are commonly formed in Indigenous undergraduate and graduate education programs. In this dissertation, I critique the notion that cohorts are necessarily safe spaces for First Nations female graduate students and argue that cohorts must be sites for coalition work and building bridges across differences both within the cohort and in mainstream contexts. I conducted initial and follow-up open-ended semi-structured interviews with 13 women with whom I had worked in First Nations educational contexts in some capacity in recent years, including as course instructor and coordinator of an educational leadership initiative. Semi-structured interviews allowed me to pursue topics raised by interviewees in some depth, and to ask them about topics raised earlier in their own or others' interviews. The women responded to queries about their educational experiences, thoughts on the beneficial and challenging aspects of cohort membership, views on the importance of First Nations curricula and pedagogy, experiences with voice and silencing in the academy, and highlights of their cross-cultural experiences. The research revealed that although participants felt that there were many beneficial aspects to their cohort membership, including the supportive environment, shared purpose, and shared sense of humor, a significant number of participants spoke about First Nations identity issues and the frequency and pain of being silenced within their cohorts as well as in mainstream classrooms. Cohort members and coordinators must articulate goals of membership that include building bridges between gulfs of difference, naming issues of power, and planning a course of action for attaining goals so that there will be a shared purpose for and among members. I argue that open cohorts offer the potential for attaining those goals.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.014 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".