Suggestions for Achieving Balance Between Corporate Dataveillance and Employee Privacy Concerns
Bibliographic record
Abstract
Big data, like most technological innovations, brings noticeable benefits as well potential risks. Dataveillance using big data is becoming another dimension in the increasing privacy concerns of the workforce. Such concerns emanate from the tension between the correct use of employee personal data and information privacy in big data within and outside the work environment. It has evolved as employees are becoming increasingly cognizant of the ways in which employers can use technologies to monitor social media activities, internet interactions, emails and other online activities outside the work environment. The objective of this research paper is to recommend a set of guidelines which will be mapped to COBIT 5 framework to help medium and large organizations balance the tension between the increasing potential of big data and employee dataveillance privacy concerns in workplaces.
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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".