Bibliographic record
Abstract
Working on an edited volume takes time, usually more than anticipated.Over this time, we have accumulated a growing list of people and institutions whose support and contributions deserve thanks and recognition.We would like to do that by identifying several of the intersecting collaborations upon which this volume builds.One collaboration involves a working group we eventually named the Research Alliance on Precarious Status (RAPS).RAPS was envisaged as a space where faculty, students, researchers in community and service-delivery organizations, union organizers, and community activists could meet regularly to discuss various kinds of work in progress on the general topic of precarious status and migrant illegality in Canada.The chapters assembled here were developed in the context of the working group's meetings, which began in September 2008 and continued every six to eight weeks, over the course of two years.a second collaboration involves conceptualizing the concept of precarious status, which was presented in an article by Luin Goldring, carolina berinstein, and Judith bernhard published in Citizenship Studies in 2009.Recognizing the specificity of the institutional production of precarious status in canada, raPs sought to develop empirical studies informed by discussions of citizenship, non-citizenship, temporary migration policies, migrant illegality, and social movements.The forum offered a stimulating space for lively and supportive dialogue on these issues.Over the period that we met, we exchanged valuable feedback; the comments of Cynthia Wright, Salimah Valiani, Patricia Landolt, and Rupaleem Bhuyan stand out.Salimah Valiani and Katharine Brasch were not always living in Toronto, yet travelled to maintain their active
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.374 | 0.212 |
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; the direct Gemma label and the distilled Codex classifier 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".