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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

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