Balanced but not fair: Strategic balancing, rating allocations, and third-party intermediaries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines how two fundamental features of many intermediaries—that intermediaries provide ratings across a set of candidates (their portfolio), and that intermediaries wish to look credible to their audiences—may create the potential for bias in evaluation outcomes. I examine how having too many positive ratings, which risks intermediary credibility, may bias an intermediary in favor of giving a subsequent negative rating, which I term strategic balancing. My setting is the ratings given by equity analysts on publicly traded firms. I find evidence consistent with a strategic balancing effect, such that having a greater allocation of high ratings in an analyst’s portfolio is associated with a subsequent negative rating, particularly when such ratings can be justified. My findings suggest that lower ratings may not be the result of poor firm performance, but instead may occur because such a rating allows an intermediary to maintain credibility. That is, the very features that define the role of the intermediary—one who interprets many market offerings for a particular audience—can create the conditions in which its evaluations may be subjective.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it