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Record W2339826991

The Irreplaceable Role of Parents in Teaching Children to Walk Safely to School

2015· article· en· W2339826991 on OpenAlexaboutno aff
Philip E. Mackey

Bibliographic record

VenueTAC 2015: Getting You There Safely - 2015 Conference and Exhibition of the Transportation Association of Canada // ATC: Destination sécurité routière - 2015 Congrès et Exposition de l'Association des transports du Canada · 2015
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsSAFERTRIPS architectureElement (criminal law)PsychologyMedical educationMedicineEngineeringPolitical scienceTransport engineeringComputer securityComputer science
DOInot available

Abstract

fetched live from OpenAlex

Safety in school zones is always a sensitive topic. An accident that involves a child on the way to school, and especially one in the school zone, will trigger a vigorous response. There is a societal expectation that the trip to school will be safe. A report published in 2011 in Quebec indicated that most accidents involving children occur at other times and places - 371 children were injured in the school year during the usual hours for trips between home and school (for the period 2003-2007), which represents 18,1 % (371/2 043) of the total number of injured children. Yet, the school remains the focus of concern. A lot of effort is undertaken by a host of agencies and organizations to try to make school zones safer. But, if an essential element is missing, are we making the most of our investments? Are we trying to compensate for the under-involvement of parents by setting up a lot of programs in other areas? A guide would at least help those parents who are trying to help their children become safe pedestrians and then safe cyclists.

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)0.012
metaresearch head score (Gemma)0.045
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: none
Teacher disagreement score0.942
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.045
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0040.005
Open science0.0020.005
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0070.001

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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueTAC 2015: Getting You There Safely - 2015 Conference and Exhibition of the Transportation Association of Canada // ATC: Destination sécurité routière - 2015 Congrès et Exposition de l'Association des transports du CanadaSame topicInjury Epidemiology and PreventionFrench-language works237,207