From the Evaluator’s Perspective: A Functional Approach to Social Judgments
Bibliographic record
Abstract
To deepen understanding of social judgments of organizations, we build on work that has adopted the evaluator’s perspective to develop a comprehensive functional approach to social judgments. We identify a set of adaptive challenges faced by evaluators in their relationship with organizations and theorize how the judgments they make can help resolve those challenges. In doing so, we clarify how social judgments are rooted in comparisons between the organization’s properties and some social referent, and extend understanding of the interrelated nature and complementary role of diverse social judgments. We explain how social judgments—such as legitimacy, trustworthiness, reputation, status, and authenticity—form a robust system of interrelated judgments that allows evaluators to collect and triangulate multiple judgments of different types, using judgment inputs from three different sources: (1) first-hand inputs, based on the evaluator’s own observations and information about the organization; (2) borrowed inputs, based on judgments made by others; and (3) taken-for-granted judgment inputs acquired through the evaluator’s socialization and education. We conclude by suggesting ways to reorient research toward unduly neglected elements of social judgment theory through systematic examination of the functional utility that social judgments provide for evaluators.
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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 | 0.067 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".