Explaining immigration casework in federal Members of Parliament's district offices in Canada
Bibliographic record
Abstract
In Canada, a majority of federal constituency offices deal primarily with immigration files. The few qualitative studies on the subject show that the resources dedicated to these files and the type of work carried out on the immigration files handled vary between offices, thus contributing to disparities in service between federal electoral districts. How can such variation be explained? Based on the quantitative analysis of unpublished administrative data, this article first highlights the diversity of files handled by constituency offices, as well as the types of intervention carried out by constituency assistants. It then aims to explain the variations in case processing according to the type of case and the volume of requests handled. Studies of constituents' files received and processed at constituency office level have argued that the political ideology, gender and ethnicity of the deputy as well as the demographics of the constituency are explanatory factors. This analysis shows that in the case of immigration files, constituency demography is the most important factor, while the MP's political affiliation plays a very limited role. These results shed new light on the factors involved in the processing of immigration cases at constituency level, and add nuance to previous, mainly qualitative analyses. Our results also contribute to understanding the work of constituency offices for constituents, which appears to be far less partisan than in other countries where similar offices exist.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".