Making Lemonade: Dealing with Analytics Surveillance in the Workplace
Bibliographic record
Abstract
In this paper we focus on how organizational practices are generated because of the introduction of analytics-based technologies aimed at monitoring employees’ performance. We use longitudinal data collected from June 2017 to December 2019 at a healthcare network in the Greater Boston Area. The related literature often points to negative aspects of workplace surveillance through these systems; what is more, it is not clear why some organizations can benefit from process improvement through analytics while other cannot. These mixed findings and a gray area around reasons underpinning the successful deployment of these systems motivate our study. We found that a mixture of top-down and bottom-up practices, in the long- term, promote collective actions of supporting the effective use of analytics. Top-down practices (management) focus on the reorganization of structures and formal processes; bottom-up practices (employees) concern cross-community bonding, creative workarounds to improve current practices and the attempt to transfer these (improved) practices to different contexts where the same analytics standards “rule.” We theorize on how these practices need to be interwoven to be successful, and we highlight that this takes time. We therefore contribute to (and question the pessimism of) related literature by showcasing the bright side of analytics at work, happening over time.
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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 | 0.028 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".