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Record W2331822834 · doi:10.1177/0022466915613592

A Socio-Cultural Analysis of Practitioner Perspectives on Implementation of Evidence-Based Practice in Special Education

2015· article· en· W2331822834 on OpenAlexfundno aff
Roxanne F. Hudson, Carol Ann Davis, Grace Inae Blum, Rosanne Greenway, Jacob Hackett, James Kidwell, Lisa Liberty, Meaghan McCollow, Yelena Patish, Jennifer Pierce, Maggie Schulze, Maya Marie Smith, Charles A. Peck

Bibliographic record

VenueThe Journal of Special Education · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
FundersMcMaster University
KeywordsConstruct (python library)Variety (cybernetics)Evidence-based practicePsychologySpecial educationWork (physics)PedagogyMedicine

Abstract

fetched live from OpenAlex

Despite the central role “evidence-based practice” (EBP) plays in special education agendas for both research and policy, it is widely recognized that achieving implementation of EBPs remains an elusive goal. In an effort to better understand this problem, we interviewed special education practitioners in four school districts, inquiring about the role evidence and EBP played in their work. Our data suggest that practitioners’ responses to policies that press for increased use of EBP are mediated by a variety of factors, including their interpretations of the EBP construct itself, as well as the organizational conditions of their work, and their access to relevant knowledge and related tools to support implementation. We interpret these findings in terms of their implications for understanding the problem of implementation through a more contextual and ecological lens than has been reflected in much of the literature to date.

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)0.088
metaresearch head score (Gemma)0.103
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.465

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0880.103
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.005
Science and technology studies0.0140.029
Scholarly communication0.0120.007
Open science0.0020.013
Research integrity0.0030.006
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 head0.310
GPT teacher head0.590
Teacher spread0.280 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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