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Record W4230381081 · doi:10.24124/2013/bpgub909

Disablement in Prince George, BC: A qualitative, holistic and participatory exploration.

2013· dissertation· en· W4230381081 on OpenAlexaffabout
Jessica E. Blewett

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsCanadian HeritageUniversity of Northern British ColumbiaLibrary and Archives Canada
Fundersnot available
KeywordsGeorge (robot)DowntownAutonomyAbleismSociologyQualitative researchCitizen journalismBuilt environmentGender studiesGeographyPolitical scienceSocial scienceHistoryEngineeringArchaeologyCivil engineering

Abstract

fetched live from OpenAlex

(dis)Abled people are frequently faced with barriers to their mobility when navigating the built environment, especially in colder climates yet little is known about this experience in northern BC. Using downtown Prince George as a study area, my research examines the lived experience of (dis)Ability in a northern, ageing, resource-based city and seeks to gain an understanding of what barriers are, how they impact (dis)Abled people, and why environments are disabling. Using go-along interviews, I found that barriers are often characteristics of the built and seasonal environment. Although generalizations cannot be made between individuals, the results suggest that barriers are connected to the presence of ableism in society and negatively impact (dis)Abled people participants described situations involving increased health issues, intense emotional stress and loss of autonomy. Exclusion, marginalization and discrimination are also uncovered as part of the lived experience of (dis)Ability in Prince George. I conclude that the first step towards an enabling environment is a social shift. --Leaf ii.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.573

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.006
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.150
GPT teacher head0.442
Teacher spread0.292 · 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 designQualitative
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
Published2013
Admission routes2
Has abstractyes

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