LATINO-HISPANIC STUDENT VOICES AND SELF-REPRESENTATION THROUGH DIGITAL STORYTELLING
Bibliographic record
Abstract
Forty percent of Portuguese and Spanish speaking students in Toronto do not complete high school (Brown, 2006). This daunting statistic motivated Pueblito Canada, a Toronto-based Non-Governmental Organization (NGO) committed to Latino-Hispanic1 children, to initiate collaboration with the local Hispanic Development Council, a community activist agency, and the Toronto Catholic District School Board (TCDSB). The three partners developed a project that engaged Latino-Hispanic students in telling their own stories of schooling. The project’s Participatory Action research (PAR) approach encompassed a series of workshops in which participating students learned the techniques of storytelling and then narrated their everyday experiences of schooling. With the support of a videographer, students moved from documenting their stories through workshops focused on their writing to producing digital stories they had authored. This article considers the emancipatory processes that facilitated students’ coming to voice. Additionally, the silences that contributed to their subjugation in the school system are problematized. Simultaneously, the participating teachers, moved by these stories as they emerged, engaged in a series of workshops that the Pueblito team called “Becoming Cultural Allies” and developed a curriculum enriching toolkit designed to provide classroom materials that reflected the historical and cultural background of their Latino-Hispanic students.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".