Workplace Surveillance in Canada: A survey on the adoption and use of employee monitoring applications
Bibliographic record
Abstract
Employee monitoring apps (i.e., 'bossware') have become increasingly affordable and accessible on the open market. Apps such as Interguard and Teramind provide companies with a powerful degree of surveillance about workers, including keystroke logging, location and browser monitoring, and even webcam usage. However, as homes have become offices, and laptops and smartphones are used for business, school, and entertainment, the increasing surveillance of 'remote work' blurs the boundaries between work and personal spaces. Drawing from an interdisciplinary study on the proliferation of employee monitoring applications (EMAs) in a nascent era of 'remote work', this paper presents findings from a survey examining Canadian companies' adoption of EMAs. The findings identify the most prevalent economic sectors that 'bossware' is currently being used within, the rationalities that underpin the ongoing use of EMAs in Canada (such as COVID-19, 'productivity/efficiency', 'cybersecurity', and 'health/wellness'), and the features of the most sought after 'bossware' apps for Canadian companies (such as time tracking, website tracking, and keystroke logging). We conclude with an analysis of how dominant surveillance discourses drive the adoption of monitoring practices, including how they inform the anticipated benefits of surveillance for the management of remote work and digital labour.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".