“If They Don’t Offer You a Seat at The Table Bring a Folding Chair”: Schooling To Produce Equitable Outcomes For Black Students in Ontario Schools
Bibliographic record
Abstract
The persistence of inequitable schooling outcomes for Black students in Ontario is unacceptable. Literature codes this student achievement disparity as the achievement gap, a race binary comparison. This research uses inequitable schooling outcomes instead shifting the burden for schooling outcomes to the schooling production systems and structures. Black Feminist Thought (BFT) and Critical Race Theory in Education (CRT) inform the theoretical and conceptual frameworks. The Delphi Technique is the method of the research. Its panel is comprised of six Black youth aged 18-24 who attended K-12 schools in Ontario. The composition of this panel challenges the status quo regarding who carries expertise. This research also supports the notion that achievement based on meritocracy is a flawed concept as it excludes from the matrix of the production systems and structures of schooling outcomes major elements such as race, gender, wealth, and health. Recommendations from the Delphi panel concerning change include making the Ontario curriculum inclusive, replacing academic streaming with a non-linear K-12 process in which students progress through school based on interest and readiness, prioritizing student belonging, rethinking and replacing the current regimen of discipline and punishment practices with student centered conflict-resolution approach, and funding schools for full inclusion. The panel also re-imagines notions of discipline and punishment suggesting the engagement of a collaborative learning approach.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".