Aspiration Performance and Railroads’ Patterns of Learning from Train Wrecks and Crashes
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
We link two influential organizational learning models—performance feedback and experiential learning—to advance hypotheses that help explain how organizations’ learning from their own and others’ experience is conditioned by their aspiration-performance feedback. Our focus is on learning from failure; this kind of learning is essential to organizational learning and adaptation, and a necessary complement to studies of learning from success. Our analysis of U.S. Class 1 freight railroads’ accident costs from 1975 to 2001 shows that when a railroad’s accident rate deviates from aspiration levels, the railroad benefits less from its own operating and accident experience and more from other railroads’ operating and accident experiences. These findings support the idea that performance near aspirations fosters local search and exploitive learning, while performance away from aspirations stimulates nonlocal search and exploration, providing a foundation for constructing more-integrated models of organizational learning and change.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it