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Record W4238616010 · doi:10.24124/2009/bpgub616

People with disabilities: Employment and assistive technology in northern British Columbia.

2009· dissertation· en· W4238616010 on OpenAlexaffabout
Kari Harder

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsCanadian HeritageUniversity of Northern British ColumbiaLibrary and Archives Canada
Fundersnot available
KeywordsWorkforceAssistive technologyExploratory researchPsychologyMedical educationMedicinePolitical scienceSociologyComputer scienceHuman–computer interactionSocial science

Abstract

fetched live from OpenAlex

With the advances in assistive technology, it was anticipated that people with disabilities would be able to participate in the workforce at a greater rate however, people with disabilities are still underrepresented in the workforce in Canada. In addition, little research has been conducted on the desire of people with disabilities to be self-employed. To explore the desire for self-employment and why the advances in assistive technology have not increased the number of people with disabilities in the workforce, a mixed methods sequential exploratory study was used. The respondents said that their disability was the main factor that prevented them from working, not the lack of assistive technology, although most were not aware of the various assistive devices that were available to them. In addition, the majority of respondents wanted to be self-employed. The results suggest that more education about assistive technology is needed.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.368
Teacher spread0.345 · 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
Published2009
Admission routes2
Has abstractyes

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