Researching the in-land refugee status determination process in Canada in light of postcolonial feminist scholars’ criticisms: reflections from my thesis
Bibliographic record
Abstract
In this presentation, I am interested in presenting the postcolonial feminist criticisms that inform my theoretical framework and how they influence my research project, specifically data collection and analysis. I particularly refer to the postcolonial feminist scholars’ criticisms of Western feminist perspectives and theories (Mohanty, 2003; Spivak, 1988; Taha, 2017). The two significant lacunae from White feminists literature are taken into consideration: 1) the little or no attention put on the notion of race and 2) “the denial of the socio-historical context that characterizes the work of Western Feminists in their approach toward “Third World” women” (Tyagi, 2014, p. 47). By way of illustration, I would like to show how I used these theoretical tools in chapter 5 of my thesis, a chapter on refugee claimants’ challenges and decisions when required to provide details about why they are seeking protection in Canada, highlighting what these tools allowed and the challenges I faced in their application.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.048 | 0.031 |
| Scholarly communication | 0.023 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".