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Record W2088496052 · doi:10.1080/19404150009546614

Outreach and support for Australian university students with learning disabilities

2000· article· en· W2088496052 on OpenAlexaboutno aff
Sali Smith, Annemaree Carroll, John Elkins

Bibliographic record

VenueAustralian Journal of Learning Disabilities · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachDocumentationEquity (law)Quarter (Canadian coin)Medical educationPsychologyAccommodationLearning disabilityMedicinePolitical scienceComputer scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract Although students with learning disabilities (LD) are increasing in Australian universities (Smith, Carroll, & Elkins, 1999), limited data is available about this group and the services available to them. This paper reports the results of a 1996 survey of university outreach, transition and orientation programs to attract potential students with LD and assist them in adjusting to higher education study. The availability of generic and specialist support services and accommodations was also investigated. Universities promote awareness of disability support services widely in outreach to prospective students and at application and/or enrolment, although students with LD are seldom specifically targeted. Formal programs to assist students from equity groups or students with disabilities to consider tertiary study are most frequently directed at high school students; only one quarter of the universities had outreach programs which might include adults with LD. Most universities offer a comprehensive range of support to students with LD through both generic and disability services. Approaches to the documentation of diagnostic assessment and the establishment of need for accommodation are however variable, and raise issues of equity which are of concern to disability support staff.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.355
Teacher spread0.309 · 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 teacher head, not a consensus.

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

Citations7
Published2000
Admission routes1
Has abstractyes

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