Bibliographic record
Abstract
Objectives or purposesThis paper will focus on how grassroots organizing and social justice mobilization can act as tool to radically transform civic education.I will be demonstrating how transformative change is enabled through acts of citizenship by immigrant women on the frontlines, as opposed to politicians.This paper will specifically be examining the grassroots organization: Jane and Finch Action Against Poverty (JFAAP) in Toronto, Canada and the ways in which they are fighting against the stigmatization of the neighbourhood, and against systemic issues of oppression and neoliberalism.The Jane and Finch area is known in the media as a highly criminalized and racialized space (Bourdreau, Keil, and Young, 2009).The residents in the community have been stigmatized in the public imaginary (Narain, 2012).Jane and Finch Action Against Poverty (JFAAP) plays a prominent role in challenging systems of power as well as advocating for social and systemic change that help reduce poverty locally and globally.Members of JFAAP are residents of the Jane and Finch community, community workers, and organizers coming from various race and class backgrounds, but most predominantly occupy the space of working class.The key questions that underpins this paper are: How does grassroots organizing serve as a means to transform civic and social justice education?How do workingclass immigrant women in Canada practice civic engagement and everyday activism in an urban environment?How does the acts of citizenship of immigrant women make critical social and political interventions to policy, education and ways of being political in Canada? Perspective(s) or theoretical frameworkMy research will be framed by using Engin Isin's (2008) theorization of 'acts of
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".