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Record W4390982474 · doi:10.1163/25888803-bja10027

The School of No Fun and No Play: Learning How to Create Supportive Environments from Children with Osteogenesis Imperfecta

2024· article· en· W4390982474 on OpenAlexaff
Brenda L. Cleary, Warren Linds, Franco A. Carnevale, Kelly Thorstad, Frank Rauch, Argerie Tsimicalis

Bibliographic record

VenueJournal of Disability Studies in Education · 2024
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsConcordia UniversityMcGill UniversityShriners Hospitals for Children - Canada
Fundersnot available
KeywordsOperationalizationOsteogenesis imperfectaSolidarityTransformational leadershipPsychologyContext (archaeology)Psychological interventionPedagogyMedical educationDevelopmental psychologySocial psychologyMedicinePoliticsPolitical science

Abstract

fetched live from OpenAlex

Abstract Children with medically complex dis/abilities hold sophisticated views on effective school leadership, adapted built environments, inspiring peer solidarity, catalyzing transformational solutions and a possible school-hospital liaison model to support student well-being. Using child-centric collaborative techniques, this ethnodramatic study engaged children with osteogenesis imperfecta ( oi ), a rare genetic disease, to create a children’s book describing their moral experiences to be disseminated to other children and families. Children living with oi experience ongoing medical interventions and dis/ability that broadly jeopardized the quality of their academic experience relative to their peers. As such, children living with oi offered trenchant insight into what supports and thwarts operationalization of their values and highlights the programs, leadership, services and paradigm shifts they feel that they need to flourish in the academic context.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.275

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.392
Teacher spread0.366 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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