Bibliographic record
Abstract
This work provides an in-depth analysis of the views of leaders of heritage language schools in Alberta. Over 25 hours of transcribed interviews and focus-group data from community heritage language (HL) school leaders and elders in the HL learning community, along with research notes were analyzed and coded for themes.14 language groups are represented. Chapter I describes my personal experience working in a HL school in Alberta. Through this experience I share how I came to the research questions that shape this dissertation. In Chapter II I review recent literature about community HL schools in North America. The theoretical lens used to interpret the data is explored in Chapter III. I have used both Bronfenbrenner’s Ecological Systems Theory and Hornberger’s Language Policy and Planning as guides so that I might understand the ecology of heritage language schools, teaching, learning and use in the province of Alberta. How the HL field in Alberta from other places is not yet documented fully. To understand and appreciate the context of HL education in the province, I have provided vignettes of the participants in my study. In Chapter IV I describe the school leaders. I classified the 11 participating school leaders into one of two groups, emerging and emerged communities, based on the length of residency of the majority of the community members and the length of the history of the school. In Chapter V I provide similar vignettes from elders in the field of HL education in Alberta. Each of the Chapters VI, VII, and VIII correspond to one of the research questions and one of the systems in Bronfenbrenner’s Ecological Systems Theory. In Chapter VI, the Microsystem, I discuss the HL leaders and HL elders thoughts of the students and teachers found in HL schools in the province. In Chapter VII, the Mesosystem, I show how leaders and elders give to their communities and to Albertan society in general through their schools. In Chapter VIII, the Exosystem, I list the multiple agencies and governmental departments that work with HL schools in the province and identify ways in which the agencies and governmental departments support or deny these schools. In Chapter IX I provide the reader with a list of recommendations which if followed would strengthen HL education, continue to support HL communities, and would further advance the Canadian concept of multiculturalism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".