An Empirical Assessment of the Intrusiveness and Reasonableness of Emerging Work Surveillance Technologies in the Public Sector
Bibliographic record
Abstract
Abstract As public sector work environments continue to embrace the digital governance revolution, questions of work surveillance practices and its relationship to performance management continue to evolve, but even more dramatically in the contemporary period of many public servants being forced to shift to remote work from home in response to the COVID‐19 pandemic. This article presents the results of three surveys, two of them population‐based survey experiments, all conducted during the onset of the COVID‐19 pandemic in Canada that compare public servant (n = 346) and citizen (n = 1,008 phone; n = 2,001 web) attitudes to various cutting‐edge—though no doubt controversial among some—digital surveillance tools that can be used in the public sector to monitor employee work patterns, often targeted toward remote working conditions. The findings represent data that can help governments and public service associations navigate difficult questions of reasonable privacy intrusions in an increasing digitally connected workforce. Evidence for Practice New work surveillance technologies are available to use within the public sector and will present acceptability challenges to public managers as they contemplate the introduction of these technologies. Multimodal survey data from Canada reveals that public servants and citizens find these emerging work surveillance technologies to be quite intrusive and unreasonable but show relatively more tolerance for digital surveillance over physical surveillance practices. Understanding surveillance anxieties among targeted employees will be key to finding a balance between employee privacy rights and employer desires to manage employees in a remote or digital environment.
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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 | 0.029 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".