Evaluative Heterogeneity: Causes, Consequences, and Future Directions
Bibliographic record
Abstract
Evaluations are ubiquitous in contemporary business, and a wide range of scholarly work has addressed how the social markers (i.e. legitimacy, status, reputation, gender, race, etc.) of products, individuals, and organizations influence average marginal perceptions of quality. Despite the potential managerial implications, only more recently have scholars directly explored the amount of consensus in such evaluations. This symposium brings together scholars interested in heterogeneity in evaluations to explore questions such as: In what settings might we expect to observe more or less consensus in evaluations? Where are the challenges and opportunities related to the measurement of consensus and its consequences? Through this panel we hope to develop “consensus” about the most promising questions in this space and the best ways to approach them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.012 | 0.034 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".