Categorical Atypicality and Evaluation Accuracy: Who Make More Accurate Evaluations of Atypical Firms?
Bibliographic record
Abstract
Abstract Prior literature on market categories and identities focuses primarily on whether categorical atypicality leads to positive or negative evaluation; however, much less is known about whether the evaluation is accurate or not. While it is important for producers to know if atypicality is penalized or rewarded, audiences are also concerned about whether their evaluation of atypical organizations is accurate, as well as how the evaluation can be improved. To shed light on this, we first test the association between categorical atypicality and evaluation accuracy. Distinguishing audiences in two dimensions – coverage coherence and typicality – we further theorize and examine which audiences are better able to evaluate atypical organizations. Analysing earnings forecasts for US firms, we find that analysts’ forecasts for atypical firms are not necessarily inaccurate, contrary to what prior theory would suggest. More importantly, the results show that analysts are more accurate in forecasting atypical firms when their coverages are more atypical and/or incoherent. The findings suggest that atypical and incoherent coverage enables analysts to better understand atypical firms, supporting our core conjecture of ‘unconventional audiences for atypical actors’.
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 | 0.015 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".