“Some people get lost”: A Practitioner Inquiry into the Transnational, Diasporic, and Educative Experiences of Participants in an After School Reading and Writing Group
Bibliographic record
Abstract
This practitioner research study documents the experiences and perspectives of nine Academic Upgrading students in an After School Reading and Writing Group who were enrolled within an urban college in Toronto. The group is composed of multilingual participants who count among their primary or secondary languages: Arabic, Bajan (A variation of West African Creole), Dhalik, English, Tigrinya, Spanish, and Urdu. These students were placed within an Academic Upgrading program to improve their literacy skills with the hopes of attaining entrance into the mainstream college. Drawing inspiration from practitioner research studies that intentionally research and “teach against the grain” (Cochran-Smith Lytle, 2009, p. 23), the After School Reading and Writing Group co-selected diasporic literature. Through discussions and writing, members recounted their own complex engagements across cultural, linguistic, and educational contexts. This research explores what happens when students narrate their own educational histories in a literary discussion group with texts that offer them an opportunity to explore the intersections between migration, race, power, identity, and schooling. I locate this work within the research on out-of-school literacies (Bronkhorst Akkerman, 2016; Hull Schulz, 2002), critical literacy (Duncan-Andrade, 2007b; Freire Macedo, 1987; Morell, 2009) and counternarratives of schooling (Delgado, 1995; Ladson-Billings, 1998; Sandoval, 2000). I document central tensions that influenced members’ educational histories and trajectories. These include literacy shaming, language policing, racial discrimination, the medical model of disability, and academic assumptions about what they could and could not accomplish. Findings surface the complexity of the group’s engagements with texts, the internalized deficit perspectives that often haunt their writing, the compulsion and reluctance to share vulnerable stories—their struggle to see themselves as legitimate writers with authorial voices. This study has implications for practitioners, policymakers, and researchers concerned with deepening understandings of teaching writing and reading to academically marginalized 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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.035 | 0.033 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".