Bibliographic record
Abstract
Exploring the motivations, practices and ethnolinguistic identities of new Gaelic speakers Outlines the history of Gaelic language use in Scotland and Nova Scotia, juxtaposing its historical decline with contemporary revitalisation efforts in both territories Situates the discrete settings of Gaelic communities in Scotland and Nova Scotia with rich ethnographic portrayals Employs quantitative and qualitative analyses to examine Scottish and Nova Scotian ‘new’ Gaelic speakers’ language practices and the motivational factors underlying these practices What are the main similarities between new cohorts of Gaelic speakers in Nova Scotia and Scotland, and what key differences distinguish them? In Scotland, public policy to support and maintain the language has increased substantially in the past 40 years. In addition to Scotland’s 57,602 speakers, however, Gaelic has persisted in Nova Scotia since the 18th century and a third of Nova Scotians are descended from families who spoke the language historically. As a response to policymakers’ language planning priorities in both polities and drawing on three years of ethnographic research in Scotland and Nova Scotia, this book presents a comparative analysis of new speaker motivations, identities and linguistic ideologies. An innovative approach to examining bilingual discourses is employed to demonstrate key distinctions and commonalities among new Gaelic speakers, with a view to informing future policy to generate greater numbers of proficient speakers on both sides of the Atlantic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".