Bibliographic record
Abstract
A l’occasion du 10e anniversaire de la revue Lex Electronica, il m’est apparu interessant de relater une aventure intellectuelle mobilisatrice au sein de l’equipe multidisciplinaire que je dirige. A cet egard, la xenotransplantation, soit le transfert de cellules, de tissus ou d’organes vivants d’origine animale a l’etre humain, se revele un exemple tres eloquent de l’evolution d’un theme de recherche. Des temps forts ont marque cette demarche intellectuelle qui s’echelonne sur presque une decennie. En retrospective, elle peut sembler rectiligne, mais au contraire, dans les faits, elle s’est averee sinueuse. C’est avec joie et fierte que je vous convie a parcourir le labyrinthe emprunte par les membres de l’equipe dediee a l’analyse des enjeux ethiques, legaux et societaux associes a l’avenement de la xenotransplantation [1]. Ensemble, nous avons examine, sous diverses facettes, un theme de recherche fascinant
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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".