Preface and Acknowledgments
Bibliographic record
Abstract
This book examines a wide array of issues related to how bilingualism has been promoted and opposed in English-speaking Canada.In the chapters that follow, I often approach these events through local, community-based case studies from across the country.When I was conducting formal interviews, and in casual conversations about my research on bilingualism, personal experiences of and attitudes toward language learning quickly came to the forefront.Both supporters and opponents of bilingualism were passionate about this issue and eager to share their opinions.Similarly, I began the research for this book with a set of questions about a topic that matters to me personally, politically, and intellectually (the three being inextricably linked).But it is historical evidence that drives the exploration that follows, and though I cannot claim complete neutrality my goal is to provide a fair and objective evaluation of the various groups and people involved in the debates over bilingualism.Inevitably, though I set aside my personal opinions and prejudices, some personal background doubtless shaped my research.I thought that starting with a short "personal case study" might shed some light on my background, perspectives, and biases.Both of my parents, children of postwar British immigrants to Canada, thought that their children should learn French.My father grew up in Toronto, Saskatoon, and Montreal.As a teenager in the west island Montreal suburb of Lachine, he attended an English Catholic high school in the 1960s and then university at the English and Catholic Loyola College (now part
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.252 | 0.140 |
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".