Bibliographic record
Abstract
L'ensemble du modele de la television est en train de se transformer grâce aux nouvelles technologies, a l'usage generalise d'Internet, au changement des habitudes des telespectateurs et a la mondialisation des industries culturelles. Tout le monde peut devenir producteur grâce au telephone mobile photographique, au camescope, au magnetophone miniature ou aux blogues qui sont a la portee de tous. Nous sommes a l'ere du cyber-journalisme, des reseaux sociaux conviviaux et des jeux video en ligne multijoueurs. Malgre tout cela, la television demeure un media important qui se conjugue desormais au je, une sorte de confessionnal a aires ouvertes ou l'on raconte son histoire, ou l'auditoire veut qu'on lui fasse voir le vrai et l'authentique; il a soif de realisme dans les telerealites, meme s'il s'agit de jeux. Bref, la television permet de rever le present. Jean-Paul Lafrance a fonde le secteur des communications en 1970 a l'Universite du Quebec a Montreal (UQAM). Il a dirige les programmes et le Departement des communications pendant de nombreuses annees et a ete titulaire de la Chaire Unesco-Bell. Auteur de plusieurs ouvrages sur les medias, il fait partie de l'equipe editoriale de la revue francaise Hermes. Il a aussi ete consultant pour plusieurs ministeres et centres de recherche ici et en Europe.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".