Surveillance at Work: An Interdisciplinary Discussion About the State of Research and Practice
Bibliographic record
Abstract
Employee surveillance, or electronic performance monitoring (EPM), refers to the now-common use of technology to observe, record, and analyze information that directly or indirectly relates to job performance. It has been nearly four decades since the earliest empirical studies of EPM, yet the literature remains fragmented and siloed across disciplines, including management, psychology, sociology, information systems, organizational theory, and law and criminal justice. Significant questions persist regarding how to best conceptualize and measure EPM. There also remains much to understand about its effects on individuals and organizations, as well as the contextual and individual factors that shape and constrain these effects. Despite these unresolved issues, the adoption of EPM continues to grow rapidly in practice, outpacing the research needed to guide its implementation. This symposium session will bring together a panel of experts in surveillance, employee monitoring, and technology to critically assess the current state of EPM research and practice, as well as identify opportunities for cross-disciplinary collaborations to integrate and advance the literature.
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 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 | 0.182 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.019 | 0.071 |
| Scholarly communication | 0.048 | 0.089 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.038 | 0.050 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".