Origins of Social Similarity Within and Across Organizations
Bibliographic record
Abstract
Organizational scholars have long investigated how homophily unfolds in organizations (Lazarsfeld and Merton, 1954; Reagans, 2005) but have tended to study the way organizational structure and practices influence social similarity in interactions (Kleinbaum, Stuart, and Tushman, 2013). Less understood are how individuals differ in their pursuit of social similiarity, and how these pursuits change within and across organizations and markets. To address these shortcomings, this symposium rallies four papers that elucidate theory and mechanisms toward this end. To begin, Parkinson, Kleinbaum, and Wheatley investigate how cognitive aspects of individuals predict friendship formation. In another paper, Abraham and Botelho unpack how entrepreneurs' behaviors regarding homophily and gender affect access to social resources. Tak and Sterling theorize how contextual factors affect the degree to which homophily is activated between organizational insiders and outsiders and test their arguments using novel data. Finally, Smith and Rand study organizational behavior in markets in order to shed light on a mechanism that affects the level of social similarity within organizations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".