Bibliographic record
Abstract
Remembering Refuge: Between Sanctuary and Solidarity is a counter-archive based on oral history interviews with people who crossed the Canada-US border to seek refuge and advocacy groups working at this border in two moments of crisis: the 1980s Central American crisis and the 2017-19 crisis at Roxham Road. This paper foregrounds counter-archiving as a methodology, building from the oral histories to illustrate how borders and bordering practices are navigated and contested and how these lived experiences push back at state-directed logics and narratives of migration. By drawing connections across past and present struggles over mobilities and borders, we offer a critical genealogy of refuge around the Canada-US border. The oral histories collectively and individually contest state-led narratives of migration as a ‘crisis,’ the need for borders to be further securitized, and specifically of the Canadian state’s generous humanitarianism towards a select few. We introduce the methodological choices, contexts, and limitations of the project’s research design, and present two themes that emerged from the oral histories: the contested element of ‘choice’ in migration movements and the important roles played by resistance and refusal in the working out of borders. Finally, we emphasize that relationships between borders are crucial to understanding the histories of asylum around this border, and the political shift activated by the counter-archive of centering borders as lived, experienced, contested or refused.
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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 | 0.065 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".