Hiding and Seeking Knowledge-Providing Ties from Rivals: A Strategic Perspective on Network Perceptions
Bibliographic record
Abstract
Rivalry is endemic in society and organizations, fueling competitive intentions and behaviors. According to social network theory, rivalry emerges among people who, like siblings, have many of the same connections to others. For this structurally equivalent rivalry to have its effects, the individual must see the other person as a rival. We ask whether, in the context of competition, people seek to identify the knowledge providers of their rivals while striving to hide their own knowledge providers from perceived rivals. We conducted two experiments that showed, for the first time, that structural equivalence does induce feelings of rivalry and does lead people to take action with respect to perceived rivals, namely to hide and seek knowledge providers. Our analysis of time-separated social network and outcome data from all 73 employees in the headquarters of a chemical company found support for these patterns of hiding and seeking in relation to perceived rivals. We also found limited evidence that career outcomes may be influenced by individuals’ success in hiding and seeking. Bringing together research on rivalry and network cognition, we provide a new approach to the strategic deployment of deception and detection in social networks.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".