Policy‐making and administrative discretion: The case of immigration in Canada
Bibliographic record
Abstract
Abstract: Decisions in complex and controversial policy areas are often left to civil servants. As a result, civil servants use their discretionary power to make decisions that will likely have important consequences for society. This is the case with immigration. Partially based on interviews conducted with Canadian and Quebecois immigration agents, the authors note that the use of the judgement of civil servants is an important factor in the selection process of newcomers. The authors recognize also that discretion can take different forms: procedural discretion, selection grid discretion, and final decision discretion. This case study enables a better understanding of both the use of judgement in the decision‐making process and the role of street‐level civil servants in the policy implementation process. Sommaire: Les décisions se rapportant à des domaines de politiques complexes et prêtant à controverse sont souvent laissées aux mains des fondionnaires. Ainsi, les fonctionnaires usent de leur pouvoir discrétiormaire pour prendre des décisions qui auront vraisemblablement des conséquences importantes pour la sociéte. C'est précisément le cas de l'immigration. Se fondant partiellement sur des entrevues effectuées par des agents d'immigration canadiens et québécois, les auteurs remarquent que dans le processus de sélection des nouveaux arrivants, le jugement de ces agents est un facteur important. Les auteurs reconnaissent également que la discrétion peut prendre différentes formes: la discrétion procédurale, la discrétion relative à la grille de sélection et la discrétion en matière de décision définitive. La présente étude de cas permet de mieux comprendre à la fois le recours au jugement dans le processus décisionnel et le rôle des fonctionnaires de la base dans le processus de mise en æeuvre des politiques.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.048 | 0.013 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".