How Inductive and Deductive Generalization Shape the Guilt-by-Association Phenomenon Among Firms: Theory and Evidence
Bibliographic record
Abstract
This study advances and tests the notion that the phenomenon of guilt by association-- whereby innocent organizations are penalized due to their similarity to offending organizations-- is shaped by two distinct forms of generalization. We analyze how and why evaluators’ interpretative process following instances of corporate misconduct will likely include not only inductive generalization (rooted in similarity judgments and prototype-based categorization) but also deductive generalizing (rooted in evaluators’ theories and causal-based categorization). We highlight the role and relevance of this neglected distinction by extending guilt-by-association predictions to include two unique predictions based on deductive generalization. First, we posit a recipient effect: if an innocent organization falls under a negative stereotype that causally links the innocent firm with corporate misconduct, then that innocent firm will suffer a greater negative spillover effect, irrespective of its similarity to the offending firm. Second, we also posit a transmission effect: if the offending firm falls under the same negative stereotype, then the negative spillover effect to other similar firms will be lessened. We also analyze how media discourse can foster negative stereotypes, and thus amplify these two effects. We find support for our hypotheses in an analysis of stock market reactions to corporate misconduct for all U.S. and international firms using reverse mergers to gain publicly traded status in the United States. We discuss the implications of our theoretical perspective and empirical findings for research on corporate misconduct, guilt by association, and stock market prejudice.
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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 | 0.039 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".