Preserving the Pronunciations of Newfoundland: Place Names: a Case Study of Bauline East
Bibliographic record
Abstract
The number of settlements scattered throughout the 230,130 square kilometres that compose Newfoundland and Labrador is shrinking rapidly.In 1921, the Newfoundland and Labrador census recorded 1489 communities.The 1991 census (the latest available) reports 314 incorporated communities and 423 unincorporated communities for a total of 737, a decrease of 50% over the past 70 years.As the number of settlements dwindles and the residents migrate, a permanent break with the past is occurring.The names of the fishing grounds and berths, of the fishing rooms, the shoals, islands, lakes, rivers, even of the settlements themselves, are not being passed to the next generation.The pronunciations of local toponyms more and more show the effects of the written word and other outside linguistic influences.Consequently, many of the pronunciation features that identify ancestral origins in different parts of England, Ireland, Scotland, France and other old world homelands are being replaced by those that are becoming increasingly similar to mainland Canadian and United States English.We feel that unless systematic programmes of collection and recording are undertaken on a large scale, and soon, great numbers of Newfoundland place names in the oral tradition, their reference points and pronunciations will be permanently lost.In 1990 and 1991, we undertook to collect, record and preserve as many of these as we could with the goal of creating a dictionary of Newfoundland place names that would provide their local pronunciations and locations.A pilot project was put in place to develop the instruments and expertise needed to undertake a province-wide programme of collecting.Field workers were sent into Placentia Bay on the western side of the Avalon Peninsula and 77 settlements were researched.Subsequently, the study area discussed below was selected to evaluate further the methodologies developed from that undertaking.In 1995, a team of field workers surveyed 100 communities in Trinity Bay on the north-west side of the Avalon Peninsula.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".