Conscientisation and the journey to self-advocacy: stories of students appropriating resilience and staying in school
Bibliographic record
Abstract
This study critiques the deficit model of examining disengagement from secondary schools and the terminology of at-risk associated with early departure from school. Using the theoretical framework of Freirian critical analysis and the praxis of conscientisation, the research in this paper suggests that youth experiencing marginalization can participate in critical reflection and student empowerment. The model of self-advocacy and collaboration developed in this study explores resilience and autonomy as a precursor to a student's re-engagement in learning communities and more inclusive involvement in what I call a synergistic model of democratic education. Interviews were conducted with grade nine and ten students from an alternative secondary school who had chosen re-engagement despite various experiences of marginalization. In an effort to appropriate a more authentic exploration of the students' existing knowledge of themselves as learners, their Ontario School Records were used as a primary resource for the students to deconstruct their own academic, social and cultural histories. The voice of these students is examined through the lens of narrative inquiry within a contemporary participative action research framework. The critical dialogue that ensued became a scaffold where the students could negotiate individual and social changes (Freire, 1999; Ecclestone, 2004; Dei, 2003), face some moments of disequilibrium (Mikesch McKenzie, 2003) in their school stories, and become more competent as problem-solvers and contributors in the conversation about the difference between schooling and getting an education. The purposes of this study are to: (1) investigate the positive understanding of resilience in an educational context; (2) explore ways that students within supportive learning communities may come to appropriate the learning strategies they need for their own success; (3) strive to understand more deeply the connections between the protective factors associated with resilience and the decision to stay in school; (4) use the hermeneutic of Freirian critical conscientisation and dialogue to shift the conversation about individual and school related factors associated with early school leavers to a more critical social and cultural analysis; and (5) finally to access strategic mechanisms within communities of resilience that facilitate the process of pre-engagement, self-directed learning and self-advocacy (Ames, 1992; Garner, 1990).
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.041 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".