Against abstraction: Reclaiming and reorienting to embodied collective knowledges of solidarity
Bibliographic record
Abstract
This article asks what kinds of places are we making within academic institutions concerned with racial and gender justice, decolonization and reconciliation when the terms of justice are fostered through the violence of abstraction? As queer scholar-activists, we share firsthand accounts of our movement from sites of community activism to the halls of academe, revealing the techniques and consequences of abstracting racist and gendered violence and dispossession. We insist on a reorientation, meaning a return to and towards, our embodied collective knowledges as sources of authority on and against the violences of settler colonialism and racial capitalism. We first recount the roots of our activism when we met in spaces of solidarity in unceded Coast Salish territories. We then describe the ways we have experienced this collective knowledge being represented back to us as we moved into academic spaces, emphasizing the epistemic violence of abstracting knowledge from its messy, hard-wrought foundations. Finally, we share strategies and experiences of reorienting ourselves toward collective and embodied knowledges in which solidarity is once again at the centre. In dialogue, we reject the violence of abstraction while asserting knowledge sovereignty in which communities maintain agency within the terms in which their lives are represented.
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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.166 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".