Bibliographic record
Abstract
A mere 150 years ago Scottish Gaelic was the third most widely spoken language in Canada, and Irish was spoken by hundreds of thousands of people in the United States. A new awareness of the large North American Gaelic diaspora, long overlooked by historians, folklorists, and literary scholars, has emerged in recent decades. North American Gaels, representing the first tandem exploration of these related migrant ethnic groups, examines the myriad ways Gaelic-speaking immigrants from marginalized societies have negotiated cultural spaces for themselves in their new homeland. In the macaronic verses of a Newfoundland fisherman, the pointed addresses of an Ontario essayist, the compositions of a Montana miner, and lively exchanges in newspapers from Cape Breton to Boston to New York, these groups proclaim their presence in vibrant traditional modes fluently adapted to suit North American climes. Through careful investigations of this diasporic Gaelic narrative and its context, from the mid-eighteenth century to the twenty-first, the book treats such overarching themes as the sociolinguistics of minority languages, connection with one's former home, and the tension between the desire for modernity and the enduring influence of tradition. Staking a claim for Gaelic studies on this continent, North American Gaels shines new light on the ways Irish and Scottish Gaels have left an enduring mark through speech, story, and song.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.016 |
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".