Bibliographic record
Abstract
Although audience perception is critical to the theory that classification affects rewards, such as ratings or sales, tests of the classification–rewards link occur without directly measuring audience perception. As a result, although a great deal is known about the mean level of rewards as a function of classification, little is known about the individual evaluations that underlie them and that contribute to the variance. I advance a process-based explanation for evaluative outcomes. Individuals make evaluations as a result of relative judgments on a subset of objects, comparing each object under consideration against a small set of others. Categorical boundaries matter to individuals perhaps because of personal preferences but also, importantly, because fit within boundaries determines how strictly to apply performance results. Simply by different individuals examining different subsets of objects, evaluative outcomes can vary dramatically, such that the same object may have different evaluations by audience members. Using recommendations by analysts at U.S. brokerages, I find support for the hypothesis that lower performance of a stock relative to other stocks already rated by a given analyst is associated with a lower likelihood of a high rating by an analyst, but this effect applies only to those stocks that fit clearly into industry boundaries. In general, the results suggest that the positive effect of category membership on evaluative outcomes, well established in prior literature, is contingent on the evaluative processes of individual audience members.
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 | 0.017 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".