Border officer training in Canada: identifying organisational governance technologies
Bibliographic record
Abstract
While recent scholarship has begun the difficult task of unpacking the sociology of frontline border policing, literature examining how frontline border officers are governed through training and organisational governance technologies is sparse (particularly in terms of how officers are trained to interact with and form perceptions of the public they serve). This article provides the first concrete examination of border officer training by conducting a Foucauldian discourse analysis of various officer training and other documents to determine the contours of organisational governance technologies and how they serve to guide border services officers (BSOs) employed by Canada Border Services Agency in interacting with and perceiving of members of the travelling public. Findings indicate that governance technologies include training documents, manuals, public policy, and a bifurcated agency governance hierarchy serving to enable, support, and constrain BSO frontline duties, public interactions, as well as potentially perceptions. Findings also reveal that officers receive very little training related to interacting with members of the travelling public on the frontline. Officers also receive very little instruction related to how they should prioritise their disparate duties related to interacting with the travelling public. Findings ultimately indicate that when training is present, governance technologies – alongside recent shifts in agency organisational governance – contain systematic biases that produce officer worldviews and social interactions that are rooted exclusively in security provision, while leaving BSOs without the tools necessary to handle other types of public interactions that regularly occur at the border.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".