Workplace Surveillance in Canada: A survey on the adoption and use of employee monitoring applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it