Bibliographic record
Abstract
Este texto apresenta a trajetória da autora como uma canadianista que tem traba- lhado para o dinamismo dos estudos canadenses na Bahia. Com uma bagagem considerável de estudos e pesquisas em universidades do Quebec, a atual vice-presidente da ABECAN conta como tem organizado eventos e publicações coletivas para reunir pesquisadores tanto no Canadá como no Brasil.Palavras-chave: Literatura quebequense; mídia; estudos canadenses; interculturalAbstract: This text presents the author’s trajectory as a Canadianist who has worked to assure the dynamism of Canadian studies in Bahia. With a rich background of studies and research in Universities of Quebec, the current vice-president of ABECAN tells how she has organized events and collective publications in order to unite researchers in Canada and Brazil.Keywords: Quebec literature; media; canadian studies; interculturalRésumé: Ce texte présente la trajectoire de l’auteure comme une canadianiste qui a beaucoup travaillé pour assurer le dynamisme des études canadiennes à Bahia. À partir d’une riche expérience d’études et de recherches dans plusieurs universités du Québec, l’actuelle vice-présidente de l’ABECAN raconte comment elle a organisé des événements et publications collectives afin de réunir plusieurs chercheurs au Canada et au Brésil.Mots-clés: Littérature québécoise; media; études canadiennes; interculturel
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.001 |
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".