<i>Writing worlds</i>: Exploring mentorship approaches supporting adolescents' authentic writing across a Canadian youth centre's programmes
Bibliographic record
Abstract
Abstract In its diverse forms, authentic writing carries the potential to connect literacy practice to an author's ‘real world’. While contemporary approaches to authentic writing instruction—advocating writer‐centred, intertextual and culturally relevant productions—are most often explored in formal learning contexts like classrooms, this paper seeks to amplify the mentorship strategies employed within an informal youth centre learning space in Montreal, Canada. The following research questions have guided this work: (1) How do adult mentors leverage authentic writing principles to support adolescents' participation within two writing‐based programmes developed in a youth centre context? (2) How do the interests, perspectives and backgrounds of adult mentors shape the mentorship strategies they choose to employ? Data collected and analysed through participant‐focused ethnographic approaches assist the author in revealing how adult writing mentors draw from their own cultural, linguistic and embodied experiences to foster interest‐driven, culturally sustaining and community‐based writing opportunities for adolescents. The four key mentorship themes emerging through this research—centring the essential role of writing journeys , identities and communities while acknowledging several barriers to authentic writing—advocate drawing from the rich literacy practices thriving within informal contexts to inform contemporary writing curricula.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.009 |
| 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".