Bibliographic record
Abstract
It was at the end of August 2020 when we both decided to launch Scholar Strike Canada, a two-day labour strike, with a series of teachins by academics, students, and staff across Canadian universities.We had both seen Twitter feeds and other social media posts and heard media reports of the scholar strikes planned across US universities to protest the police killings of George Floyd and other Blacks in the United States.The political resurgence that was taking place globally led by Black, Indigenous, racialized, queer, trans, and two-spirited youth and students motivated us to create this labour action with teach-ins on anti-Black, anti-Indigenous racialized and settler colonial violence that encouraged faculty, students, and staff to pause their teaching, learning, and labour for two days.The teach-ins featured Black and Indigenous faculty and activists of colour such as Rinaldo Walcott, Bonita Lawrence, Eve Tuck, Andrea Davis, Courtney Skye, Chris Ramsaroop, and many others.This labour action that we staged on 9 and 10 September 2020 provided faculty and students a space to engage the global racial and anticolonial reckoning that had taken place in Canada during most of the spring and summer.Many of us participated in the uprisings organized by Black Lives Matter and other Black activists to protest police brutality and killings of Black, Indigenous, and racialized peoples here in Canada during the COVID-19 outbreak and lockdown.The Scholar Strike Canada teach-ins allowed for bold conversations and critiques of the white supremacist neoliberal university and its subjugation of radical, Black, Indigenous, and decolonial knowledges and practices.Many academics, activists, and students had seen their university's statement on ending anti-Black racism and other forms of racism and recognized it as a statement of performance and not of Foreword
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.435 | 0.401 |
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".