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 organizational 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 organizational 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 prioritize 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 organizational 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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".