Pushing privileged pillars in Canadian universities
Bibliographic record
Abstract
Canadian federal discrimination legislation excludes social class which leaves poverty, or “social condition” (MacKay & Kim, 2009), out of universities’ equity, diversity, and inclusion (EDI) mandates. By refusing to widen access and participation (WAP) for students whose lives are shaped by poverty, Canadian universities are complicit in perpetuating poverty across generations. This seven-month community-based participatory action research project used conversations, focus groups, case studies, and integrated knowledge mobilization. Research Sisters, Canadian women with lived experiences of poverty who accessed an undergraduate degree at a Canadian university, explored: 1) Why they are an important demographic to Canadian universities, and 2) How university leaders can create non-deficit-based and decolonial WAP for poverty-class people. Four key themes were discovered: First, conversations centered on the voices of participants through the building of trust and the creation of the Underclass Sisterhood of Solidarity, based upon the late feminist scholar and activist María Lugones’ (1987) theory of “world”-travelling with “loving” versus “arrogant” perception. Second, research Sisters refused to justify their social class worthiness to be in university (Phillips, 2021). Third, knowledge democracy was central to honour the knowledge systems and cultures of poverty-class students. Fourth, research Sisters used their experiences to work towards creating a grassroots social innovation model to teach university leaders how to create sustainable WAP solutions that address structural classism and poverty discrimination from an intersectional framework. This research demonstrates the need for radical imagination in crafting WAP solutions for poverty-class students. There are four spaces that might inform WAP initiatives: 1) Radical imagination space, 2) Emotional and spiritual space, 3) Material, cultural and social support spaces, and 4) Epistemological space. These spaces must be created by and for those with lived experiences of poverty. Furthermore, the (gendered nature of) poverty discrimination in Canada must be addressed through legislation where social class is included in equity, diversity and inclusion (EDI) mandates.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.083 | 0.024 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".