An Examination of the Factors that Influence Whether Newcomers Protect or Share Secrets of their Former Employers*
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
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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