An Examination of the Factors that Influence Whether Newcomers Protect or Share Secrets of their Former Employers*
Bibliographic record
Abstract
abstract This research investigated the factors that influence a decision that is often faced by employees who have made a transition from one organization to another: the decision about whether to protect secrets of their former employer or to share them with their new co‐workers. A total of 111 employees from two high‐tech companies participated in interviews. Their comments were analysed and, based on both relevant literature and the results of that analysis, a theory of the factors that influence newcomers' protect vs. share decisions was developed. According to that theory, newcomers first decide whether or not information is a trade secret of their former employer by considering (1) whether the information is part of their own knowledge, and (2) whether the information is publicly available, general, and negative (about something that did not work). If newcomers decide the information is a trade secret, they then evaluate (1) the degree to which their obligations are biased towards their former or new employer, and (2) the degree to which they identify more strongly with their former or new employer. Newcomers whose obligations and identifications are biased towards a new employer are more likely to share secrets. If these obligations and identifications are balanced, newcomers may share information in a way that allows them to believe they are fulfilling their responsibilities to both their former and their new employers.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".