Learning Through Rare Events: Significant Interruptions at the Baltimore & Ohio Railroad Museum
Bibliographic record
Abstract
The collapse of the roof of the Baltimore & Ohio (B&O) Railroad Museum Roundhouse onto its collections during a snowstorm in 2003 provides a starting point for our exploration of the link between learning and rare events. The collapse occurred as the museum was preparing for another rare event: the Fair of the Iron Horse, an event planned to celebrate the 175th anniversary of American railroading. Our analysis of these rare events, grounded in data collected through interviews and archival materials, reveals that the issue is not so much what organizations learn “from” rare events but what they learn “through” rare events. Rare events are interruptions that trigger learning because they expose weaknesses and reveal unrealized behavioral potential. Moreover, we find that three organizing routines—interpreting, relating, and re-structuring—are strengthened and broadened across a series of interruptions. These organizing routines are critical to both learning and responding because they update understanding and reduce the ambiguity generated during a rare event. Ultimately, rare events provoke a reconsideration of organizational identity as the organization learns what it knows and who it is when it sees what it can do. In the case of the B&O Railroad Museum, we find that the roof collapse offered an opportunity for the organization to transform its identity from that of a museum to that of an attraction.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".