Social Pitfalls At Work: Mistaken Beliefs About Maximizing Workplace Social Value
Bibliographic record
Abstract
Much of employees’ professional success and emotional well-being comes from their social interactions in the workplace. Unfortunately, employees sometimes fail to socialize as effectively as they could, reducing their social capital at work and limiting the potential benefits they could gain from building strong social connections in the workplace. This symposium demonstrates four new ways that employees fail to maximize their social value at work, and additionally suggests a reason why they do so: workers have mistaken forecasts regarding their social interactions. In particular, the symposium showcases four distinct contexts of social interactions – talking to dissimilar others, seeking help, gossiping, and being humorous – and suggests methods for improving social capital and consequently career success. Taken together, these symposium presentations shed light on the various pitfalls, mistaken beliefs, and surprising ignorance we have when it comes to optimal workplace socialization. The research findings will encourage people to examine their own assumptions regarding social interactions at work, so that they can create more effective connections and uplifting moments, and achieve greater social capital for themselves in the workplace. A Closer Look at Homophily: Why Do People Avoid Talking to Dissimilar Others? Author: Erica Boothby; The Wharton School, U. of Pennsylvania Author: Gus Cooney; Harvard U. Should I Ask Over Zoom, Phone, Email, or In-Person? Communication Channel and Predicted Compliance Author: Vanessa Bohns; Cornell U. Author: Mahdi Roghanizad; Ted Rogers School of Management, Toronto Metropolitan U. Gossipers Beware: Gossipers Underestimate the Negative Reputational Consequences of Gossiping Author: Andrew Choi; U. of California, Berkeley Author: Sonya Mishra; U. of California, Berkeley Author: Juliana Schroeder; U. of California, Berkeley The First Laugh: It is Easier Than We Think to Attempt Humor with Strangers Author: Elizabeth Jiang; UCLA Author: Sanford Ely DeVoe; UCLA
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".