Bibliographic record
Abstract
When the Royal Bank of Canada was recently caught up in a maelstrom of bad publicity over its use of temporary foreign workers, it led politicians and pundits to scrutinize and question the growing use by Canadian firms of imported, short-term labour. The Royal Bank was accused of misusing a system designed to help employers who could not find Canadian workers by using it, instead, to find cheaper foreign labourers to replace higher-cost Canadians. But the incident raises a bigger question than simply how one bank makes use of Canada’s Temporary Foreign Worker Program (TFWP): Whether the program is, in fact, interfering with the natural supply and demand responses of the labour market. And if we want to make better use of available Canadian labour, the time has come for the federal government to start cutting back on the use of TFWP. The number of admissions under the TFWP has nearly tripled in 25 years, from 65,000 to 182,000 in 2010. The primary justification for the expansion of the program has been the widespread assumption that Canada is suffering from a growing shortage of labour. Yet, it is hard to find any evidence to support this belief. What Canada is arguably facing is a widening imbalance between the skills that the labour market demands and the skills that workers are equipped with, as well as between where the jobs are and where available workers live. There are looming worker shortages in certain sectors while other sectors have an overabundance of available workers. Improving the balance in the labour marketplace does not require an increase in the labour supply. Indeed, the TFWP is sometimes being used to fill jobs with foreign workers in regions that already suffer from relatively high unemployment rates. Temporary Foreign Workers could be distorting the labour-market forces that would bring together more Canadian workers and jobs. A strategy is needed to better equip Canadian workers with the education, training and skills that employers are looking for, and to find ways to better mobilize unemployed workers from regions with lower demand for workers, to provinces with a greater need for workers. With no evidence that any increase in immigration is necessary, Ottawa should consider holding the current immigration rate steady, and re-evaluate only when the business cycle warrants — possibly returning to the disused policy of increasing immigration rates in boom times, but lowering them during slower economic periods. Admissions under the Temporary Foreign Worker Program should be reduced, and provinces should bring some influence to bear on universities and colleges to align their programming, and the incentives to draw students into certain programs, more directly with labour-market needs. Finally, the government must find ways of inducing Canadian workers — through tax holidays or other measures — to move from high-unemployment regions to the provinces where the jobs are.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.044 | 0.021 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.020 | 0.031 |
| Insufficient payload (model declined to judge) | 0.016 | 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".