The Contaminating Effects of Building Instrumental Ties
Bibliographic record
Abstract
In this paper, we examine the consequences of social networking for an individual’s morality, arguing that the content and approach of networking have different implications for how a person feels during the development and maintenance of social ties. We focus in particular on professional-instrumental networking: the purposeful creation of social ties in support of task and professional goals. Unlike personal networking in pursuit of emotional support or friendship, and unlike social ties that emerge spontaneously, instrumental networking in pursuit of professional goals can impinge on an individual’s moral purity—a psychological state that results from viewing the self as clean from a moral standpoint—and thus make an individual feel dirty. We theorize that such feelings of dirtiness decrease the frequency of instrumental networking and, as a result, work performance. We conducted four studies using both field and laboratory data from different populations to investigate the psychological consequences of networking behaviors. Two experiments provide support for a causal relationship between instrumental networking for professional goals, feeling dirty, and need for cleansing. A survey study of lawyers in a large North American business law firm offers correlational evidence that professionals who experience feelings of dirtiness from instrumental networking, relative to those who do not, tend to engage in it less frequently and have lower job performance. With regard to sources of variability in dirtiness from instrumental networking for professional goals, we document that when those who engage in such networking have high versus low power, they experience fewer feelings of dirtiness. An additional experimental study constructively replicates this finding.
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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 | 0.010 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".