Bibliographic record
Abstract
Most of the data used in this book is taken from police reports and hospital and trauma databases.Each of these databases has its limitations, and for the most part each under-represents the actual numbers of people killed and injured in motor vehicle crashes in Canada.In addition, police-reported data, when used to capture the contributing factors of crashes, can be understated by as much as 20 to 25 percent due to missing data elements and unknown coded variables.Although the author has made every effort to obtain the most up-to-date data possible, most of the police-reported data covers only the years up to 2008, despite that much of it was received in 2013.Throughout this book, then, wherever statements indicate the number of people killed or injured in some recent time period (e.g., in the last ten years), the specified period typically corresponds to a period ending in 2008.In almost all cases, the corresponding endnote provides the exact years as well as the source of the data.The terminology used in the book is drawn from diverse sources, including research studies, books, government and university reports, and interviews with experts from around the world, and it is intended to illustrate key road safety concepts using everyday language for laypersons as well as road safety practitioners.As such, some terminology may differ from that found in technical guidelines, manuals, and other sources used by practitioners in Canada.This book is not designed to provide technical guidance but rather to show, overall, how Canada could eliminate deaths and serious injuries from its roads while revealing the nature and scope of many of the changes needed.The word "pedestrian" should be interpreted to mean any person outside of a motor vehicle excluding a pedal cyclist.A pedestrian, therefore, xv
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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.280 | 0.219 |
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".