Writing Skills and Students’ Aspirations to Pursue Post-Secondary Educational Opportunities
Bibliographic record
Abstract
This study highlights potential positive impacts of tutoring writing to marginalized youth at a community service after-school programme. Although many secondary school students possess language fluency and competency, youths from low-income and/or minority cultures may experience unequal access to essential writing instruction in school. Students’ potential for academic advancement depends on the procurement of written language “codes” necessary to familiarize them with the manners, culture and language of power. Writing tutoring responds to the individual learning needs of youth at-risk; tutoring programmes are effective in supporting the advancement of their goals and values and for their integration into society. Interview data from nine students, staff and tutors at Pathways, Lawrence Heights and observations, field notes and journal entries from my position as researcher and volunteer tutor, reveal that the dialogic nature of tutoring facilitates an unconstrained interchange of ideas, which generates knowledge applicable to the students’ particular needs, and fosters a bond between the tutor and tutee unlike the teacher/student relationship experienced in the school context. My investigations explore how one-to-one tutoring for teaching writing skills to youth at-risk might be especially conducive to improving writing effectiveness because the tutor’s non-evaluative and non-autocratic responses are personalized to the contexts, situational needs and nuances of each student. By acknowledging the unequal distribution of writing proficiency among Ontario’s students and the inequities within the system, educators and policymakers may look for occasions in the writing curriculum that address students’ perceptions of themselves as thinkers and learners. With a broadened appreciation for the impact of tutoring programmes and their relevance to students’ pursuit of post-secondary opportunities, collaboration between researchers, schools and their communities, a thoughtfully developed educational agenda that supports our students is possible.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".