Bibliographic record
Abstract
In this chapter, the author reflects on different storied moments contouring the crooked paths she traversed in her doctoral journey. She adopts a narrative lens that enables her to inquire into these experiences as means of making visible the unique plotlines that she has lived as a South Asian female learner, educator, and researcher living in Canada. Interwoven within this chapter are artful images and poetic musings representative of the ways in which she has been storied by herself and others in different worlds. Alongside epistemic and ontological considerations, contemplated separately and together, each of these autobiographically narrated moments are indicative of the complexity and messiness involved in undertaking such an educational venture. Unlike the master narrative in Western society, which suggests that a doctoral journey is a clear-cut path, the storied moments taken up in this chapter illuminate some of the challenges and tensions a racialized female can encounter in attempting to learn and grow within different worlds of education, research, and academia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.026 |
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; both teacher heads agree on what is shown here.
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".