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Record W4361268817 · doi:10.1352/1934-9556-61.2.95

A Tale of Two Adaptations of a Special Education Advocacy Program

2023· article· en· W4361268817 on OpenAlexaff
Meghan M. Burke, Samantha E. Goldman, Chak Li

Bibliographic record

VenueIntellectual and developmental disabilities · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsAssumption University
Fundersnot available
KeywordsEmpowermentSelf-advocacyQualitative researchProgram evaluationPsychologyAdaptation (eye)Special educationIntellectual disabilityProcess (computing)Medical educationPublic relationsPedagogyPolitical scienceSociologyMedicineComputer sciencePublic administration

Abstract

fetched live from OpenAlex

Special education advocacy programs support families to secure services for their children with intellectual and developmental disabilities. Although research demonstrates the efficacy of one such program (the Volunteer Advocacy Project), its effectiveness when replicated by others is unknown. Replication research is critical to ensure that programs can remain effective. The purpose of this study was to explore the adaptation process for two agencies that replicated an advocacy program. Quantitative and qualitative data were collected to examine feasibility, acceptability, and effectiveness. Although it took resources to replicate the advocacy program, agencies reported ongoing implementation would be easier once adaptations were completed. The adapted programs were effective in increasing participants' knowledge, empowerment, advocacy, and insiderness. Implications for research and practice are discussed.

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)0.020
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 score0.008
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.046
GPT teacher head0.342
Teacher spread0.296 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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