Watching the Clock: Time-Tracking and the Erosion of Privacy and Dignity at Work
Bibliographic record
Abstract
This article examines the rise of time-tracking technologies as a dominant form of digital workplace surveillance and their implications for workers' privacy and dignity. Often framed as neutral tools for enhancing productivity, these systems embed continuous algorithmic oversight into daily work routines, intensifying managerial control and reshaping the employment relationship. Drawing on legal, philosophical, and socio-technical scholarship, the article argues that privacy and dignity are mutually reinforcing values, both of which are undermined by pervasive surveillance practices. Privacy is not treated here as a waivable entitlement grounded in individual consent, but rather as a structural safeguard that protects autonomy and supports workers' collective identity and rights. Dignity, in turn, requires that workers be treated as individuals rather than instruments of output. Time-tracking technologies challenge both by normalising constant monitoring, restricting discretion, and reducing labour to data. The article critiques the reliance on individual consent as a regulatory safeguard, highlighting its inadequacy in the context of structural power imbalances. It calls for recognising privacy and dignity as non-waivable rights requiring collective and institutional protections. A regulatory framework based on worker representation in surveillance-related decisions is proposed, shifting oversight away from unilateral employer control. By foregrounding the normative stakes of algorithmic management, the article calls for a measured reassessment of labour law's role in protecting autonomy and dignity in the digital workplace.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".