Bibliographic record
Abstract
Regardless of decades-long social and political advocacy aimed at reversing the damaging discriminatory practices embedded in Canadian laws, societal beliefs, and cultural practices, Canadian BIPOC communities have not achieved equality, nor are they treated equitably. Thus, the current research project intended to critically evaluate and expose the systemic nature of racism by examining the information presented in a recent online conference: The Scholar Strike Canada. In this conference, several BIPOC scholars dispersed their knowledge and their demands for equity via teach-ins that occurred on September 9th and 10th 2020. By conducting a qualitative content analysis of the sessions, five major themes emerged as key foci of BIPOC advocates in Canada: systems of oppression, the institution of policing, lived experiences of BIPOC, the white settler state, and finally resistance. These findings speak to the many disparities that BIPOC survive through in Canada and the US and provide an opportunity to learn about how BIPOC scholars see racism and other forms of institutional oppression to impact their lives. The results and findings demonstrate how racism is a systemically embedded issue in Canada, particularly where the criminal justice system and education systems intersect.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.035 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".