Bibliographic record
Abstract
In Immigrants and the Labour Force Ravi Pendakur considers whether today's immigrants are more upwardly mobile than those who came to Canada earlier, whether they face discrimination in the labour force, and whether refusal to recognize credentials earned before migrating hurts life chances in the new country.He looks at the roles post-war immigrants have played in Canada's urban labour force and the ways these roles have changed in response to changes in intake policy and economic conditions, exploring these issues in the context of two changes that have dominated immigration and labour-force patterns for the last fifty years.First, Canada's primary source for immigrants has shifted dramatically from the United Kingdom and Europe to countries outside Europe.Second, there has been a remarkable transformation in the nature of work: Canada's economy has changed from relying on resource extraction to an emphasis on manufacturing, and currently is emerging as post-industrial and knowledge-based.Pendakur combines an analysis of parliamentary debates on immigration issues with an evaluation of the regulatory and policy changes that resulted from these discussions and an analysis of how the work of immigrants changed over five decades.He then provides both a political and quantitative analysis by looking at issues that affect not only immigrants but minorities born in Canada in order to assess the degree to which labour market discrimination exists and whether employment equity programs are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.681 | 0.472 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".