Bibliographic record
Abstract
In the opening sequence of a 2008 documentary,Ni Sauvage, Ni Barbare(Neither Savage Nor Barbaric), co-produced by Québecois and Moroccan television, the director Roger Cantin introduces his subjects over images of art, ritual, and nature that alternate between northern Canada and northern Africa: Fouad Lahbib is a painter from Morocco. He is Berber, he is Amazigh, he is an autochthon from North Africa. Florent Valiant is a singer from Quebec. He is Innu, he is Amerindian, he is an autochthon from North America. At first glance, they come from completely different cultures. Their ancestral lands are far apart, separated by an ocean; they don’t look at all alike. One people travels by rivers and through immense forests. The other lives with heat and drought. What do these two men, Fouad Lahbib and Florent Vallant, have in common? They belong to marginalized cultures whose extinction was precipitated, whose assimilation was desired, and whose language and customs were silenced. Were they really savages and barbarians? Or simply people who approach the world with a spirit of harmony, sharing, and solidarity? Meeting each other for the first time, Fouad Lahbib and Florent Vallant will learn with us how much all men are alike, wherever they may live.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".