MétaCan
Menu
Back to cohort
Record W3116317123 · doi:10.16993/sjdr.730

Services, Systems and Policies Shaping the Built Environment for People with Mobility Impairments

2020· article· en· W3116317123 on OpenAlexaff
Sigrún Kristín Jónasdóttir, Snæfríður Þóra Egilson, Jan Miller Polgar

Bibliographic record

VenueScandinavian Journal of Disability Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsWestern University
Fundersnot available
KeywordsCLARITYUsabilityLegislationDisabled peopleBusinessPublic policyAffect (linguistics)Internet privacyPublic relationsPolitical sciencePublic administrationComputer scienceSociologyPsychologyLawApplied psychology

Abstract

fetched live from OpenAlex

Background: For people with mobility impairments, access to the built environment is essential to their community mobility. Services, systems and policies shape accessibility and affect the opportunities people have to participate in society.Aim: To gain an understanding of the accessibility policy of the built environment in Iceland through an exploration of policy documents.Method: Public policy documents regarding accessibility from official websites of local and national authorities in Iceland were collected and reviewed.Findings: This review summarizes policies and identifies critical concerns that need to be addressed to improve access to the built environment in Iceland: (1) inconclusive or incomplete information, (2) limited clarity in legislation and guidelines, (3) limited users’ involvement in policymaking, (4) insufficient monitoring of services and (5) limited fit with usability values. All those aspects are critical to ensure and protect disabled people’s rights to move around and participate in society.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.210
GPT teacher head0.481
Teacher spread0.271 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueScandinavian Journal of Disability ResearchSame topicAssistive Technology in Communication and MobilityFrench-language works237,207