An Experimental Study of Influential Elements on Cyberloafing from General Deterrence Theory Perspective Case Study: Tehran Subway Organization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cyber-loafing is a virtually new phenomenon from the old problem of loafing at work places. The internet has alone made remarkable changes in today’s organizations, although has brought many concerns and pitfalls for efficiency and effectiveness in working hours. This study, by the means of General Deterrence Theory and rational choice theory, examined the role of rules and regulations against cyber-slacking and the effect of detection and past enforcement of punishments in Tehran subway organization. The results of this study revealed that severe regulations against cyber-loafers will decrease the intention to cyber-loaf. Moreover, the existence of appropriate detection mechanisms like internet monitoring systems, the awareness of past enforcement of strict retributions among employees, and abusiveness perception of a particular internet activity will substantially lower the chance of being involved in internet abuse in work places.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it