Preconditions, Regulatory Failure and Corporate Negligence Behind the Lac-Mégantic Disaster
Bibliographic record
Abstract
The Lac-Mégantic oil train disaster, July 6, 2013, was not a highly improbable, impossible-to-anticipate event. A number of prior conditions, the product of deliberate regulatory and corporate actions and inactions, contributed to the risk of a major accident. These preconditions include: three decades of railway deregulation under Conservative and Liberal governments under which railways gained increasing freedom to regulate themselves; a weakened, dysfunctional regulator and a flawed safety regime; a negligent company with repeated safety violations and penchant for cutting corners; a regulation-adverse , austerity-minded government indifferent to the growing dangers posed by the increase in the transportation of oil-by-rail; and an industry bent on blocking or weakening potential protective regulations affecting its costs. These preconditions provided the context for a series of mutually reinforcing regulatory failures, which accumulated, and as oil-by-rail grew, so too did the prospects of avoiding an accident diminish, to the point where the question became: when, where and how serious.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".