Bibliographic record
Abstract
After the horrific and deadly train explosion at Lac-Mégantic, Que. in the summer of 2013, there are serious questions being raised publicly about the safety of Canada’s rail-transport system. Unfortunately, Canada’s public rail-safety data are currently in no shape to provide the answers to those questions. When Canadians ask, as many have in recent months, whether the rail-transport system is “safe,” they surely want to know whether the accident record is low — compared to other countries and to other forms of transport — and whether it has been improving or getting worse over time. Yet, the statistics that might provide the answers are worryingly inaccessible, sometimes conflicting, and in certain cases not available at all. The inability to publicly monitor airline safety statistics would be considered unacceptable. Yet trains transporting volatile goods across Canada arguably expose entire communities, as in Lac-Mégantic, to potentially catastrophic dangers. How is it, then, that the Transportation Safety Board, Transport Canada and Statistics Canada do not even publicly report something as basic as the number of train trips made every year in Canada? Nor do their statistics distinguish between incidents and accidents involving passenger trains and those involving freight trains. And how is it that the total number of accidents in some years is reported differently by these various monitoring organizations? If Canadians are, as it appears, destined to see increasing volumes of goods, specifically dangerous goods, transported by rail, it is that much more important that the federal government significantly improve the reporting of rail-safety data. It is not only vital that our railroads are safe; it is just as vital for the public to have information showing exactly how safe they are.
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 | 0.040 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.028 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".