Bibliographic record
Abstract
Engaging with Indigenous legal traditions brings to light the existence of different forms of legal conscience. The Indigenous legal traditions catalyse both the ontological questioning and its response. And they also offer a response to the critique of law and the evolution of legal practice. Approaching different legal traditions requires, however, a change of perspective. This reflection considers the insights of anthropology, linguistics, literature, translation and semiotics as applied to law. Towards a ‘shared framework’ and ‘common legal sense’, the semiotic approach enables us to visualise the legal landscape, beyond the borders of modern constituted forms, on a wider horizon of legal communication. It allows us to approach the narrative semiotics of different legal traditions, such as the dances, storytelling, artefacts like Wampum belts and protocols for ceremonies in Indigenous law. Furthermore, reconnecting legal traditions contributes to recalling, re-embodying and reconnecting the legal subject with the more-than-human realm – reconstituting the legal experience in its integrity. Beyond the operation of translation, what is at stake in the evolution of the legal language and practice is the constitution of a common semiotic space, a space of legal communication and understanding.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.056 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".