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Record W2768376868 · doi:10.31542/j.ecj.1229

If You Want to Get Away with Murder, Use Your Car

2017· article· en· W2768376868 on OpenAlexaffvenueabout
Heather Magusin

Bibliographic record

VenueEarth Common Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsMacEwan University
Fundersnot available
KeywordsBlamePedestrianPerceptionCriminologySuicide preventionPolitical scienceEmpirical evidencePublic discoursePublic healthPoison controlSociologyPsychologyEnvironmental healthSocial psychologyLawEngineeringMedicineTransport engineeringPolitics

Abstract

fetched live from OpenAlex

The persistently high rate of pedestrian and cyclist road deaths in Canada is a major public health concern and a serious impediment to encouraging active transport. Despite empirical evidence that cyclist- and pedestrian-targeted policies like helmet laws and jaywalking tickets do not decrease fatalities, popular discourse continues to put the onus on vulnerable road users, often blaming them for their deaths. The negative effect of victim-blaming on vulnerable communities has been well established in the critical and feminist traditions, while recent studies have begun to examine the effects of negative discourse on cycling uptake and safety. To examine how public discourse reflects and affects the perception of blame in vulnerable road user deaths, this paper critically analyses news articles of pedestrian and cyclist fatalities in Edmonton in 2016. [results and analysis] Policy implications and avenues for future research are also discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.718

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.004
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.056
GPT teacher head0.344
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2017
Admission routes3
Has abstractyes

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