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Record W3022948701 · doi:10.1080/15021149.2020.1758990

Higher education and behavior analysis in Europe: creating a unified approach for the training of autism professionals

2020· article· en· W3022948701 on OpenAlexaboutno aff
Lise Roll‐Pettersson, Αngeliki Gena, Sigmund Eldevik, Paolo Moderato, Zuilma Gabriela Sigurðardóttir, Karola Dillenburger, Mickey Keenan, Shahla Ala’i-Rosales

Bibliographic record

VenueEuropean Journal of Behavior Analysis · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationCertificationContext (archaeology)Higher educationCzechPolitical scienceGlobalizationTraining (meteorology)Quality (philosophy)European commissionMedical educationPublic relationsPsychologyEconomic growthEuropean unionBusinessMedicineEconomic policyEconomicsGeography

Abstract

fetched live from OpenAlex

Training of behaviour analysts for autism services, has improved notably within a European higher education context. However, regional discrepancies associated with economic, health care, social services, and institutional policies magnify the importance of creating appropriate unified training and consumer protection. Although the European Association for Behaviour Analysis (EABA) has endorsed the Behavior Analyst Certification Board’s (BACB) designations, the absence of European and national regulations, recognition, and accreditation remain significant barriers to quality training and implementation. These challenges are particularly pertinent in light of BACB decision to limit certification to residents in the USA and Canada after 2022. Advances, challenges, and future directions are discussed within the context of higher education in the United Kingdom, the Czech Republic, Greece, Iceland, Italy, Norway, and Sweden. The post-Bologna European agenda for higher education, globalization and opportunities for the training of behaviour analysts within European higher education are outlined.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.367
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.110
GPT teacher head0.361
Teacher spread0.251 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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