Can a Classroom Be a Family? Race, Space, and the Labour of Care in Urban Teaching
Bibliographic record
Abstract
This article reports on findings from a case study of an eighth-grade teacher in an innercity school in downtown Toronto, Canada. It investigates the teacher’s pedagogical use of the metaphor of “family,” using interview data to underscore the effects produced by such an operating logic in a classroom. Methodologically, the article puts forward a novel analytic strategy to keep in dynamic interplay the relationship between how a teacher conceptualizes her teaching practice and where she locates those ideas. By focusing in-depth on one teacher’s pedagogical relations in the classroom, the article aims to better understand how teachers position the ubiquitous notion of “care” in their practice and how they enact “community” in their classrooms and in the larger schools and neighbourhoods in which they work. In this case study, the concepts and experiences of race and space are considered centrally in the examination of a racialized teacher’s pedagogical practices in a diverse and socio-economically marginalized school. The study has important implications for teacher education, inviting us to more explicitly acknowledge the salience of race in our conceptions of “care” and the investment of time and emotion that is demanded when practising politically conscious caring in teaching.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.033 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".