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Record W7097828975

The Roles of Emerging and Conventional Technologies in Serving Children and Adolescents with Special Needs in Rural and Northern Communities

2016· article· en· W7097828975 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsRural areaEmerging technologiesProfessional developmentSpecial needsEarly childhood education
DOInot available

Abstract

fetched live from OpenAlex

More than a century of Canadian and international experience and research in open and distance learning indicates that traditional and emerging technologies can be used effectively, alone or in combination, to provide access to services and education for adults and children living in rural and northern communities. How-ever, although there is an emerging literature about children with special needs, technology, and the North, it is at a preliminary stage and is fragmented across many professional communities, one of which is increasingly the field of open and distance learning. Research studies, pilot projects, and written reports need to be expanded and shared as a matter of priority. With appropriate research support and policy review, the promise of digital technologies can be realized to serve children with special needs, their families, teachers, and health care providers who live in rural and northern communities. Résumé Plus d’un siècle d’expériences canadienne et internationale et de recherche rela-tives aux systèmes d’apprentissage ouvert et en ligne nous ont enseigné que les hautes technologies et les technologies traditionnelles peuvent être utilisées effica-cement, seules ou ensemble, pour offrir l’accès aux services et à l’éducation pour les adultes et enfants vivant dans des communautés rurales et du Nord. Cepen-dant, malgré l’existence d’une littérature émergente relative aux enfants ayant des besoins particuliers, à la technologie et au Nord, celle-ci en est encore au stage préliminaire et est éparpillée dans de nombreuses communautés professionnelles, en particulier dans le champ de l’apprentissage ouvert et à distance. Les re-cherches, projets pilotes et rapports écrits doivent être développés et partagés prioritairement. Grâce au soutien à la recherche et à une révision des politiques appropriées, les potentialités des technologies numériques peuvent être réalisées, au service des enfants ayant des besoins particuliers, ainsi que leur famille, profes-seurs et fournisseurs de soins de santé habitant dans les communautés rurales et du Nord.

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)0.004
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.021
GPT teacher head0.327
Teacher spread0.306 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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