Bibliographic record
Abstract
The study of a Canadian ethnocultural community, however small, has far-reaching historical, sociological and human implications.The preparatory research, however uncertain or incomplete, has produced an abundance of documentation which will remain invaluable.The lists of names, places, professions as well as of testimonials regarding living and working conditions, of hardships and successes, were overwhelming.But they provide a sound statistical base for the in-depth understanding of the over-all growth of the community.They are also a base for further studies.This type of study, though limited, offers an opportunity to explore the nature and meaning of emigration/immigration, or migrations in general.It provides insight into a complex social phenomenon: in both the country of origin and in the country of destination, or more specifically, in the receiving communities, and in those towns robbed of their «best young people», of their most active polulation.! The study of a limited area and of a small community relates to the history of Canada and Italy.It also fills a gap in the patchy history of emigration, since, according to scholars, the data related to this modern phenomenon is often unavailable, complex and inconsistent.? Thus, the study of a community or group helps clarify, corroborate, or even reshape some general trends established by historians for longer periods of migration.Beyond the statistics, facts and charts, there is the
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".