Understanding Law Among Other Rationalities: Worlds of Meanings at Conflict. An Interdisciplinary Study of Truths and Believes in Law, Science, Religion, and Indigenous Ways of Being
Bibliographic record
Abstract
The research discusses conflicting rationalities in three papers respectively addressing: law and scientific expert testimony; law and Indigenous healing traditions; and law and religion in cases involving commercial arbitration and Indigenous concerns. The purpose of this research project is twofold. On one hand, we wish to point out the complexity of cases where apparently different rationalities—understood as 'worlds of meaning'—are at stake. We therefore explore the affinity, likeness, aversion or competition that can exist among divergent rationalities, as well as within one single rationality. We also study the possible misidentifications and intertwinements of the same. On the other hand, our respective perspectives show how the ways in which conflict of rationalities are theorized may depend on the worlds of meaning that are actually studied and question whether such discrepancies make sense. The research therefore connects the theme of the conference to the broader issue of multi-layered worlds of meaning, including Indigenous ways of being in Canada. It also aims to encourage further reflection and debate on the appropriateness of language and categories of law in contemporary occidental countries and the feasibility of challenging these, as a fruitful area of future research in law and society.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.021 | 0.135 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".