Bibliographic record
Abstract
This article discusses how student disengagement is conceptualized by English-speaking youth attending English urban public schools in Montreal, Quebec. School dropout is theorized as being a culminating event in a process of school disengagement (Rumberger, 2011 Rumberger, R. W. (2011). Dropping out: Why students drop out of high school and what can be done about it. Cambridge, MA: Harvard University Press.[Crossref] , [Google Scholar]). Using 2 qualitative methods (maps and interviews) in a grounded theory approach (Charmaz, 2014 Charmaz, K. (2014). Constructing grounded theory: A practical guide through qualitative analysis (2nd ed.). Thousand Oaks, CA: Sage. [Google Scholar]), a theory of disengagement is presented and supported by existing literature in student engagement and school dropout. Student disengagement is framed from a socio-ecological perspective (Lawson & Lawson, 2013 Lawson, M. A., & Lawson, H. A. (2013). New conceptual frameworks for student engagement research, policy, and practice. Review of Educational Research, 83(3), 432–479.[Crossref], [Web of Science ®] , [Google Scholar]) in a move away from its predominant conceptualization as an individual trait. In doing so, we highlight some issues of urban education in Montreal, addressing such themes as inequity, low-income status, experiences of failure and the pass/fail paradigm, the elementary/secondary school transition, normativity, and, finally, the public/private distinction in schooling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".