Bibliographic record
Abstract
Abstract Critical legal scholars and activists do a lot with law: we ‘trash’ it, we resist it, we dissect it, we occasionally (reluctantly) appeal to it — we also laugh at it, albeit less systematically. This laughter, I argue, is valuable — and we would benefit from making a method out of it. I show how this might be done with reference to a rambling and ongoing legal story: the 56 judgements related to Indigenous and allied opposition to Canada’s notoriously controversial Trans Mountain Pipeline expansion project. Interpreting this as a chain novel of sorts (with a cast of characters that would put Tolstoy to shame), I demonstrate how engaging with law through a comic lens can help us ‘deal’ with the seeming immovability of modern Western law’s most self- (and other-) destructive tendencies in the context of the climate crisis. This comic mode/mood of engagement — characterised by flexibility and life-orientation — stands in contrast to the rigidity and linearity of the tragic vision that dominates modern Western law. The comic vision shows us that we can engage fruitfully with a tragic, coercive law by declining to take it seriously — because, ultimately, this law can be ‘tricked’ into serving higher objectives than its own rule.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.041 | 0.155 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".