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Record W3110508668 · doi:10.22230/ijepl.2020v16n15a987

Education Leaders’ Perspectives on Special Education Research: A Priority Setting Study

2020· article· en· W3110508668 on OpenAlexafffundvenueabout
Jennifer Baumbusch, Jennifer E. V. Lloyd, Yu Chyi David Liou, Danjie Zou

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGraduation (instrument)Psychological interventionMedical educationCategorizationSurvey researchPsychologySet (abstract data type)Educational researchPublic relationsPolitical scienceMedicinePedagogyNursingApplied psychology

Abstract

fetched live from OpenAlex

Research priority setting, an element of knowledge mobilization, makes knowledge users integral to the development of research agendas. To date, the use of research priority setting in educational research has been minimal. The purpose of this study was to explore educational leaders’ perspectives on research priorities in special education. We conducted a cross-sectional research priority setting survey with educational leaders from 60 public school districts in British Columbia, Canada. Seventy-one participants completed the survey. Results of a pre-set list of questions indicated that the top three research priorities were: grade-to-grade transitions, high school graduation, and time to designation. In terms of designation, or student categorization, participants were most interested in “Intensive Behaviour Interventions/Severe Mental Illness.” When asked about other priorities, participants identified research on types of support/interventions. These results have implications for developing a research agenda that can support informed decision-making around policy-development and programming for students with special needs.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.458
GPT teacher head0.552
Teacher spread0.094 · 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 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

Citations3
Published2020
Admission routes4
Has abstractyes

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