Méthodologies ambidextres en droit (Ambidextrous Legal Methodologies)
Bibliographic record
Abstract
Dans cet ar ticle, j’elabore une metaphore pour aborder la recherche qui se trouve au carrefour du droit et du cinema. Je qualifie cette approche d’ambidextre. Dans la premiere partie, je discute des modes interdisciplinaires de recherche juridique et je propose quelques lignes directrices en matiere de travaux explicitement transdisciplinaires. Je decris ensuite la maniere dont mes methodes ambidextres s’efforcent d’etre transdisciplinaires. Mon travail est ancre dans le droit et le cinema, tout en se deplacant audela de ces disciplines, afin de faire emerger un sens par rapport a la marginalisation des personnes vivant en situation d’itinerance. In this research, I elaborate a metaphor (ambidexterity) to describe research located at the law/film nexus. I discuss interdisciplinary models of legal research and provide a few guidelines for producing explicitly trans-disciplinary work. I describe how I deploy ambidextrous methods and methodology to explore the legal marginalization of street-involved people.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".