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Record W4243772460 · doi:10.24124/2020/59132

Issues of accessibility in the north: An analysis of transportation and the highway of tears

2020· dissertation· en· W4243772460 on OpenAlexaff
Cassidy Shuvera

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsGeographyGeorge (robot)PoliticsPerspective (graphical)Highway systemTransport engineeringPolitical scienceEngineeringHistoryLaw

Abstract

fetched live from OpenAlex

The Highway of Tears is a term that is known across northern British Columbia. Since 1969, women and girls have gone missing and been found murdered along the 724 km stretch of Highway 16 between Prince Rupert and Prince George, British Columba. Many of these women were trying to get from one destination to another when their safety was compromised. These individuals were from communities with few transportation options available and where hitchhiking is a common form of transportation. Using an interdisciplinary perspective including northern studies, political science, and women’s studies I will analyze the issues of transportation in the north in connection to the Highway of Tears case. In particular I will examine how the Highway 16 Transportation Action Plan developed in order to determine what happened and why it took ten years for a policy response to emerge that addressed safety concerns and transportation gaps in the north.

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)0.002
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 score0.183
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0140.010
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.297
Teacher spread0.282 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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