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Choice as an Aspect of Quality of Life for People With Intellectual Disabilities

2009· article· en· W2053573041 on OpenAlexaff
Ivan Brown, Roy I. Brown

Bibliographic record

VenueJournal of Policy and Practice in Intellectual Disabilities · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsUniversity of VictoriaUniversity of Toronto
Fundersnot available
KeywordsConceptualizationFreedom of choiceSet (abstract data type)Quality (philosophy)Management scienceQuality of life (healthcare)PsychologyChoice setComputer scienceEngineering ethicsEpistemologyArtificial intelligenceEngineeringPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract Choice, a concept included in the quality of life approach, is frequently referred to in quality of life and related literature, but its components have not been described clearly. Drawing on conceptual considerations and research reports, the authors review and extend what is known about choice, and set out a conceptualization of its two main components: available opportunities and choice‐making. The most important characteristics of opportunities are breadth and familiarity, and the most important characteristics of choice making are freedom, initiative, and skill. The authors consider the application of choice to supports and services by discussing numerous practical issues and providing suggestions for application. These are summarized as an overall four‐step strategy for moving forward that sets the scene for more specific strategies to be developed and evaluated.

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)0.010
metaresearch head score (Gemma)0.018
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.009
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.002
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 head0.103
GPT teacher head0.450
Teacher spread0.347 · 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

Citations130
Published2009
Admission routes1
Has abstractyes

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