Fixing the Blame: Organizational Culture and the Quebec Bridge Collapse
Bibliographic record
Abstract
The collapse of the Quebec Bridge in 1907 remains "one of the world's major structural failures," writes Eda Kranakis in "Fixing the Blame: Organizational Culture and the Quebec Bridge Collapse," one still studied by engineering students as an object lesson. But the common understanding of what caused the partially completed bridge to collapse is flawed, Kranakis argues. The Royal Commission that investigated blamed errors in judgment by two engineers, Theodore Cooper and Peter Szlapka, shaping a view of events that has prevailed ever since. But its conclusions are belied by a mountain of evidence collected by the commission itself during the inquiry and contributed by engineers and public officials subsequently. "The combined weight of this evidence suggests, rather, that the errors behind the collapse were rooted in the project's organizational culture." Kranakis links organizational factors to three crucial technical errors that the commission found to be responsible for the bridge's collapse, and uses that discussion to lead into the broader question of how organizations influence engineering. "One of the ironies of the Quebec Bridge disaster," she concludes, "is that the important organizational lessons it offeredÑalthough understood by many engineers and public officials at the time of the eventÑhave since been forgotten because later analysts accepted the Royal Commission's narrow interpretation of error and causation."
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".