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Record W4361268786 · doi:10.1352/1934-9556-61.2.110

Applicants to a Special Education Advocacy Training Program: “Insiders” in the Disability Advocacy World

2023· article· en· W4361268786 on OpenAlexaff
Brittney L. Goscicki, Samantha E. Goldman, Meghan M. Burke, Robert M. Hodapp

Bibliographic record

VenueIntellectual and developmental disabilities · 2023
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsAssumption University
Fundersnot available
KeywordsSocial connectednessInsiderPsychologyConstruct (python library)Self-advocacySocial psychologyQualitative researchScale (ratio)Public relationsDevelopmental psychologyPedagogyPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

Although social groups have "insiders," this construct has not been measured within the disability advocacy community. Examining 405 individuals who applied for an advocacy training program, this study examined the nature of insiderness within the disability advocacy community and ties to individual roles. Participants showed differences in mean ratings across 10 insider items. A principal components analysis revealed two distinct factors: Organizational Involvement and Social Connectedness. Non-school providers scored highest on Organizational Involvement; family members/self-advocates highest on Social Connectedness. Themes from open-ended responses supported the factors and showed differences in motivation and information sources across insiderness levels and roles. Qualitative analysis revealed two additional aspects of insiderness not addressed in the scale. Implications are discussed for future practice and research.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.112
GPT teacher head0.390
Teacher spread0.279 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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