Relational theory and critical race theory as social practice in school: The restorative approach
Bibliographic record
Abstract
This thesis presents a restorative approach in education as a relational, anti-racist and culturally responsive way of being together in school communities. Through a theoretical lens of relational theory and critical race theory, case study research was conducted on the restorative approach at a diverse urban elementary school in Halifax, Nova Scotia. The questions this work addresses include: How does a restorative approach affect the quality of relationships in school communities, particularly for people that have been marginalized? And, what are some recommendations on how to best implement restorative approaches effectively and sustainably? Participants in the case study include students and staff at the school which serves a socioeconomically and culturally diverse population, including an African Nova Scotian community. In addition, the reflexive nature of the researcher is taken into account as she is a member of the school community as both a classroom teacher and resident. Qualitative data was collected through group talking circles with students and staff at the school and analyzed to reveal four themes that are integrated around a core category of nurturing social culture change including 1)
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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.070 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".