Bibliographic record
Abstract
Ways of Belonging examines the experiences of undocumented young people who are excluded from K–12 schools in Canada and are rendered invisible to the education system. Canadian law doesn’t mention the existence of undocumented children, and thus their access to education rests on discretionary practices and is often denied altogether. This book brings the stories of undocumented young people vividly alive, putting them into conversation with the perspectives of the different actors in schools and courts who fail to include these young people. Drawing on long-term ethnographic fieldwork, Francesca Meloni shows how ambivalence shapes the lives of young people who are caught between the desire to belong and the impossibility of fully belonging. Meloni pays close attention to these young people’s struggles and hopes, showing us what it means to belong and to endure in contexts of social exclusion. Ways of Belonging reveals the opacities and failures of a system that excludes children from education and puts their lives in invisibility mode. An interview with the author (https://www.qmul.ac.uk/clpn/news-views/book-interviews/items/interview-with-francesca-meloni-about-her-book-ways-of-belonging-undocumented-youth-in-the-shadow-of-illegality.html)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".