Bibliographic record
Abstract
We have all worked with them - colleagues who get ahead by taking credit for another person’s work or who take our ideas and present them as their own. For anyone on the receiving end of this behavior, it can feel as though a theft has occurred. As with a theft of physical goods, a theft of ideas can elicit highly negative reactions and behaviors. However, despite widespread acknowledgement that people have their ideas and work efforts stolen, this phenomenon is yet to be studied empirically. We explore the concept of knowledge theft – the deliberate act of claiming unjustified ownership of the work contributions of another – and propose that the central and defining features for those who experience knowledge theft are loss of knowledge and the loss of acknowledgement or recognition for that knowledge that would have otherwise been received if not claimed by another. We then present four studies establishing the validity and reliability of a measure of knowledge theft, differentiating it from other deviant workplace behaviors, and demonstrating the incremental validity of knowledge theft in predicting relationships with a variety of negative work-related outcomes. Additionally, we find that the relationship between knowledge theft and negative workplace outcomes is exacerbated when the degree of loss perceived by targets is greater. Our research lays the foundation for further study of knowledge theft from the perspective of targets who lose out on the recognition for their ideas and efforts and from perpetrators who steal ideas and recognition from others.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| 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".