English-French bilingualism in Quebec : the acquisition of literacy skills
Bibliographic record
Abstract
During the time when I was working on this thesis I obtained help and support of many people.So it is my innermost wish to express my gratitude and thanks.Ich möchte mich ganz herzlich bei allen meinen Freunden und Freundinnen bedanken, die mich ermutigt haben, nach Montreal zu gehen um dort für meine Diplomarbeit zu recherchieren, und die Motivation, die sie mir gegeben haben.I would also like to express my gratitude to Dr. Fred Genesee who invited me without hesitation to come to the McGill University in Montreal for the summer of 2010 to do my research there and who guided me during my six-week-stay.De plus, je suis reconnaissante aux étudiants et aux diplômés de Montréal qui ont participé à cette enquête et complété les questionnaires.Un très grand merci aussi à tous les gens au Canada qui m"ont soutenu pendant mon séjour à Montréal.I also warmly thank Dr. Annemarie Peltzer-Karpf for having me write under her supervision and for her academic advice.Je remercie également Sébastien, mon copain, qui m"a aidée en me soutenant beaucoup durant la phase finale de rédaction de ce mémoire.Abschließend möchte ich mich ganz herzlich bei meiner Familie bedanken, besonders bei meinen Eltern, Maria und Gottfried Reisner, die immer an mich geglaubt haben und mich stets in allen meinen Vorhaben unterstützt haben.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".