Race, power and social action in neighbourhood community organizing: a case study
Bibliographic record
Abstract
This thesis asks the following question: "how does race and ethnicity emerge in the daily practice of community organizers who work in low-income, multi-racial, multi-ethnic neighbourhoods?" Given that the concepts of race and ethnicity are understood to be social constructs, community organizing practice is analysed in this thesis in terms of its' constitutive role. By examining community organizing practice in one neighbourhood in Québec, Canada, I argue that issues of race and ethnicity are largely constructed in community organizing practice as distinct from relations of power. I demonstrate this construction of race and ethnicity using data gathered from 16 community organizers through interviews, textual analysis and observations. I analyse the data from three angles: first, actions regarding issues of race and ethnicity that are normalized (i.e. "possible"); second, actions regarding issues of race and ethnicity that are constrained (i.e. "not possible"); and, lastly, actions that are resistant to normalized and/or constrained practices, and that link race and ethnicity to power relations. In this way, I delineate Foucault's "field of action" (1982, p. 221) regarding race and ethnicity in neighbourhood community organizing and demonstrate how the structure of power in community organizing functions to render the connection between race and power largely invisible.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.033 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 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".