Bibliographic record
Abstract
In nearly every social and economic setting individuals evaluate one another. Evaluations are a necessary component for status attribution, rewards and penalties, and impact the social, economic, and psychological welfare of individuals. By implication, evaluation has considerable impact on organizational processes and outcomes. In this paper, we focus attention on the effects of the evaluator’s social network on evaluation outcomes. We present results from a field intervention in which we implement a peer-to-peer based evaluation system in a business school setting. Our model includes both ascriptive and relational (e.g., friendship) characteristics of the evaluator-evaluated dyad, as well as the social structure within which the dyad is embedded. Using this fine-grained dataset we find strong evidence that connections matter, and that peers affect evaluation outcomes. Consistent with our predictions, individuals with broad networks rich in open triad counts receive more favorable evaluations. By contrast, evaluators with more open triad counts give more negative evaluations of their peers, suggesting that broad networks have opposing effects, contingent on an individual’s role as evaluator or evaluated.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".