Elegies and Laments in the Nova Scotia Gaelic Song Tradition: Conservatism and Innovation
Bibliographic record
Abstract
Gaelic-speaking emigrants brought with them a massive body of oral tradition, including a rich and varied corpus of song–poetry, and many of the emigrants were themselves highly skilled song-makers. Elegies were a particularly prominent genre that formed a crucially important aspect of the sizeable amount of panegyric verse for members of the Gaelic aristocracy, which is a tradition dating back to the Middle Ages. This contribution will demonstrate that elegies retained a prominent place in the Gaelic tradition in the new world Gaelic communities established in many parts of Canada and in particular in eastern Nova Scotia. In many respects, the tradition is a conservative one: there are strong elements of continuity. One important difference is the subjects for whom elegies were composed: in the new world context, praise for clan chiefs and other members of the traditional Gaelic aristocracy were no longer of relevance, although a small number were composed primarily out of a sense of personal obligation for patronage shown in the Old Country. Instead—and as was increasingly happening in the nineteenth century in Scotland, as well—the deaths of new community leaders, including clergy, and other prominent Gaels were recorded in verse. The large number of songs composed to mark the deaths of community members is also important—particularly young people lost at sea and in other tragic circumstances, occasionally in military service, and so forth. In these song–poems, we see local poets playing a role assumed by song-makers throughout Gaelic-speaking Scotland and Ireland: that of spokespeople for the community as a whole.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".