A Survey of the Attitudes of Employers \nin Newfoundland and Labrador toward \nthe Recruitment and Employment of \nNew Canadians and International Workers
Bibliographic record
Abstract
The study was based on a random telephone survey of Newfoundland and Labrador businesses and a second survey which focused on employers who were known to have hired international workers or New Canadians. Results of the survey indicated that firms have had some difficulty finding skilled workers in the last five years, and they expect that there will be a more pronounced skilled labour shortage within the next twenty years. \n \nLess than 10% of the random sample had employed New Canadians or international workers in the last five years. Many indicated that they hire from the local labour market, and many had not received an application from the immigrants. These businesses indicated that they did not see a labour cost advantage to hiring New Canadians and international workers. They also felt that recruitment and training costs would be higher, particularly with respect to language barriers, and that these costs might not be recovered because these workers may not stay in Newfoundland and Labrador. \n \nThe majority of firms who hired New Canadians and international workers reported a positive experience. Many of the negative responses were related to concerns local labour should be hired first. Local business would hire New Canadians and international workers if there were incentives in place, and most businesses were not aware of the incentives currently available. \n \n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".