Noise as Signal in Learning from Rare Events
Bibliographic record
Abstract
Firms increasingly have access to information about the failure events of other firms through public repositories. We study one such repository that accumulates reports of adverse events in the medical device industry. We provide qualitative evidence that shows how firms select a sample of adverse events and then engage in inferential learning. We show that firms use the reports of others to extract new valid knowledge from the adverse events in other firms. We use quantitative evidence to explore how a public repository can be used to provide more direct evidence of vicarious learning. Our findings challenge some standard assumptions about vicarious learning. First, we show that the learning in a repository does not come from referent others. Instead, it emerges directly from failure events that might ordinarily be dismissed as noise. Second, we show that the learning does not come from copying others. Instead, it is constructed by firm members as they assemble individual failure events to identify possibilities they had not considered. Third, in contrast to vicarious learning, where the referent others and rare events provide the context, repository-based learning requires that actors impose their own context as part of the learning process. Our qualitative and quantitative evidence serve explanatory purposes by showing how firms use a repository of failure events to identify moments of valid learning, and they serve exploratory purposes by investigating how we can demonstrate reliable learning from a repository of failure events. The e-companion is available at https://doi.org/10.1287/orsc.2017.1179 .
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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 | 0.019 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".