Lessons from #Occupy in Canada: Contesting Space, Settler Consciousness and Erasures within the 99%
Bibliographic record
Abstract
Under a slogan of ‘We are the 99%’, the #occupy movement has won praise for its bold reclamations of public space and for re-centring class analysis in North America. Despite this, however, important critiques of the movement’s elisions and erasures have also beenraised. This article examines how three #occupy encampments in Canada have engaged with these calls to #decolonise the movement and to address divisions within the 99%. These critiques question #occupy’s ability to fix a ‘broken social contract’, ‘reclaim Canada’, or ‘take back our democracy’ without addressing the underlying racial contracts foundational to North American settler-states. Practical experiences with raising postcolonial critiques are examined through in-depth interviews with organisers at #occupy encampments in Montréal, Toronto and Vancouver.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.080 | 0.045 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".