In Search of Meaning: Does the <i>Fortune</i> Reputation Survey Alter Performance Expectations?
Bibliographic record
Abstract
Abstract Our study theoretically and empirically examines performance antecedents and consequences of the Fortune annual Survey of Corporate Reputation. Accounting‐and market‐based measures of performance are used to predict the raings, and investor reactions to the publication of the ratings are predicted to be associated with the extent to which the ratings diverge from antecedent predictions. Lower‐than‐predicted ratings should generate a negative response while higher‐than‐predicted ratings should generate a positive response. Contrary to expectations, we found a negative relationship. In addition, this negative relationship was only for the lower‐than‐predicted ratings. For higher‐than‐predicted ratings the relationship with investor reaction was insignificant. Résumé Notre étude consiste en un examen théorigue et empirique des facteurs influençant le classement annuel du magazine Fortune et des conséquences de ce classement sur la performance des firmes évaluées. Nous utilisons des mesures comptables et financières pour examiner le lien entre la performance et la réputation de la firme. La façon dont les investisseurs réagissent à ces révaluations doit en principe être proportionnelle au degré de divergence par rapport aux prédictions antérieures. Théoriquement, les évaluations qui sont moins élevées que prévues entraînent une réaction négalive des investisseurs, tandis que les évaluations qui sont plus élevées que prévues entraínent une réponse positive des investisseurs. Mais dans la réalité, on observe plutôt une relation inverse, en l'occurrence dans le can des évaluations qui sont moins élevées que prévues. Les évaluations qui sont plus élevées que prévues n'ont qu'un impact limité sur la réaction des investisseurs.
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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 | 0.011 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".