Bibliographic record
Abstract
Scholars of culture, humanities and social sciences have increasingly come to an appreciation of the importance of the legal domain in social life, while critically engaged socio-legal scholars around the world have taken up the task of understanding in all of its cultural, political, and economic dimensions. The questions arising from these intersections, and addressing imperialisms past and present forms the subject matter of a special symposium issue of Social Identities under the editorship of Griffith University's Rob McQueen, and UBC's Wes Pue and with contributions from McQueen, Ian Duncanson, Renisa Mawani, David Williams, Emma Cunliffe, Chidi Oguamanam, W. Wesley Pue, Fatou Camara, and Dianne Kirkby. This paper introduces the volume, forthcoming in late 2007. The central problematique of this issue has previously been explored through the 2005 Law's Empire conference, an informal but vibrant postcolonial legal studies network.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".