Bibliographic record
Abstract
Abstract The notion of the ‘new speaker’, and its salience particularly in relation to minority language sociolinguistics, has become increasingly prevalent in the last decade. The term refers to individuals who have acquired an additional language to high levels of oracy and make frequent use of it in the course of their lives. Language advocates in both Scotland and Nova Scotia emphasise the crucial role of new speakers in maintaining Gaelic on both sides of the Atlantic. As a result, Gaelic language teaching has been prioritised by policymakers as a mechanism for revitalising the language in both polities. This article examines reflexes of this policy in each country, contrasting the ongoing fragility of Gaelic communities with new speaker discourses around heritage, identity, and language learning motivations. Crucially, I argue that challenging sociodemographic circumstances in Gaelic communities in Scotland and Nova Scotia contrast with current policy discourses, and with new speaker motivations for acquiring higher levels of Gaelic oracy in North America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".