Multiplex Ties and Knowledge Sharing: Effects of Tie Formation Order
Bibliographic record
Abstract
Prior research shows multiplex ties to be consequential for many outcomes. The potential path dependencies of multiplex tie formation, and their effects on these outcomes, have not yet been examined. Original qualitative research suggests that the order of tie formation may influence the type of trust dominating a multiplex tie. Categorizing workplace ties as either instrumental or expressive, multiplex ties originating as instrumental ties (i.e., I-E ties) are likely to be dominated by cognition-based trust, and multiplex ties originating as expressive ties (i.e., E-I ties) by affect-based trust. Given the known effects of trust on knowledge sharing, we examine whether multiplex ties can have different knowledge sharing consequences, depending on the initial tie type. We test for distinctive effects of I-E versus E-I ties on knowledge seeking and giving using an online vignette study. Although we find no differences between I-E versus E-I multiplex ties, we do find significant differences in the importance of multiplex ties on knowledge sharing. Multiplex ties yield higher likelihoods of knowledge giving than instrumental or expressive ties alone. Surprisingly, only the presence of an instrumental tie - independent from the presence or absence of other ties - yields higher probabilities of knowledge seeking.
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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 | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".