Bibliographic record
Abstract
The founding of a formal education system in Canada was influenced by religious, racial and classist biases and bigotry that continues to perpetuate within and throughout our institutional systems today; creating barriers of access, practices of othering and underrepresentation in areas that economic, social and academic advancement of specific populations within Canadian society. This final research paper will explore the history of education in Canada, identify whom are identified as underrepresented student populations and explore early Postsecondary education (PSE) intervention access initiatives for underrepresented populations offered in publicly funded colleges such as Centennial College, George Brown College, Humber College and Seneca College, which are located in Toronto. This paper will explore the complex intersectionality amongst underrepresented groups, barriers to accessing and navigating PSI systems, explore intersectoral partnerships that encourage and support early PSE intervention planning as well identify and critique social capital gains through early PSE intervention initiatives. This paper will argue that throughout Canada’s history the explicit and implicit practice of exclusion has created a systemic system of underrepresentation of Indigenous, marginalized (including Black, 2SLGBTQ+, first-generation and females), French speaking and economically disenfranchised student populations within the PSE system. This underrepresentation has resulted in the call for early access intervention initiatives that support equitable PSE accessibility that acknowledges the intersectionality of individuals identified within these underrepresented populations as well as eliminating the legacy of institutional bigotry and exclusive practices within the fabric of the Canadian education system.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.743 | 0.458 |
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".