Youth Unemployment and Immigration: A Case Study of Ontario, Canada
Bibliographic record
Abstract
This study investigates the long-run relationship between youth unemployment and net immigration in Ontario, Canada where youth is defined as ages between 15-24. Two different models are estimated based on different definitions of youth. An Auto regressive Distributed Lag (ARDL) framework is used to establish the direction of causation between the variables. The study concludes a long-run relationship between youth unemployment and immigration. The estimation of the long-term coefficients shows that there exists a long-run relationship between youth unemployment and immigration, irrespective of the age cohort, showing that a 1% increase in immigration will lead to a 0.4% and 0.3% increase in youth unemployment for Model I and Model II respectively. Youth unemployment is likely to be affected by other factors as well such as government austerity measures, adult unemployment rates and overall economic situation. Therefore this study can be further extended to include various other relevant variables. Given the specificity of our research question, time limitations and data availability these factors were not considered in our research. It can be further expanded to include other Canadian Provinces as well.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".