An Exploration of the Lived Experiences of Students who attended Supervised Alternative Learning Programs in Southwestern Ontario
Bibliographic record
Abstract
According to Statistics Canada (2021), in Ontario, 27.75% of young people age 15 to 24 attended but did not complete high school in 2020. This figure has increased in the last few years. Indeed, Statistics Canada reported that in 2005, 21% of young Ontarians did not complete high school. As a response to this worrying trend, the Ontario Ministry of Education (OME) implemented the System Alternative Education system (AES): a system that uses out-of-school locations as substitute-learning settings for students who may not be able to participate in regular classroom learning. One component of the Alternative Education system (AES) is the Supervised Alternative Learning (SAL) programs (Ontario Ministry of Education, 2005; Ontario Ministry of Education, 2010). Despite the fact that SAL Programs have been in place for over 15 years in Ontario, there is no published research that can provide a clear understanding of the experiences of students in these programs. Furthermore, very little is known about how students who attended these programs reflect on their educational experiences. This research examines the experiences of former students who attended SAL programs. A key rationale for this study is the urgent need to fill the void in the academic literature, as well as to bring attention to the experiences of marginalized students in the education system. This study found that a meaningful academic curriculum, a positive classroom space, courses geared towards further academic opportunities and future employment capabilities, as well as teacher preparedness, are perceived as key influences in student high school graduation. It is recommended that school boards develop and provide suitable services to this population, so that they can graduate and be successful in their future employment/ educational endeavours.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".