(In)Equity and Academic Streaming in Ontario: Effects on Students and Teachers and How to Overcome These
Bibliographic record
Abstract
This study is concerned with equity and academic streaming in Ontario K-12 education. The purpose of this study is to investigate the ways in which teachers’ reflections on their professional experiences can deepen our understanding of academic streaming and its impacts on students in Ontario, with a focus on: a) factors, other than academic ability, that determine a student’s stream; b) the effects of academic streaming; and c) how the education system, particularly vis-à-vis streaming, might be improved to better serve students and teachers based on these findings. Using semi-structured interviews with practising educators, this study serves to extend and consolidate existing research on streaming, which tends to be largely negative with regards to how academic streaming works in practice and the effects that it has on students. This study finds some benefits to academic streaming, mostly for teachers and students within the academic stream, however, most findings strongly suggest that our current system of academic streaming is highly inequitable, particularly due to the influence of socioeconomic status, race, and level of parental advocacy on which stream a student takes. This study strongly suggests that academic streaming is largely detrimental since it segregates students and puts many “on a pathway…that closes a lot of doors.” Another significant finding is that participants were keen to increase student integration using approaches such as destreaming. Other methods to make destreaming feasible are also suggested, such as reducing class size and increasing teacher collaboration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".