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Record W2981847704 · doi:10.5206/cie-eci.v48i1.9339

Le Plan d’Intervention au Canada et en Europe : Une Analyse Comparative Entre Cinq Systèmes Scolaires

2019· article· en· W2981847704 on OpenAlexaffvenueabout
Philippe Tremblay, Enkeleda Arapi, Nathalie Bélanger, Piercarlo Bocchi, Sabine Kahn, Marie Toullec-Théry

Bibliographic record

VenueComparative and International Education · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of OttawaUniversité Laval
Fundersnot available
KeywordsTerminologyPlan (archaeology)PedagogyIntervention (counseling)Political scienceSociologyPsychologyGeography

Abstract

fetched live from OpenAlex

In North America, as well as in Europe, most countries have included in their educational policies the possibility of an individualized educational project for students with special needs during their compulsory schooling. The tool used for this is mainly an individualized education plan (IEP) set up for students experiencing academic or behavioural difficulties at school. The purpose of this article is to take a comparative look at individualized education plans from five school systems: Quebec, Ontario, France, Belgium (Wallonia), and Switzerland (Ticino canton). A comparative analysis was conducted on the IEP frameworks from these school systems. This comparative analysis sheds light on the terminology, definitions, characteristics, and components of IEPs used in five European and Canadian school systems for students with difficulties. The results show that the terminology used is specific to each school system, but the importance of planning, integration, collaboration, coordination, teaching interventions/arrangements is highlighted in all these definitions. The analysis reveals differences in the components of the IEPs among the five school systems studied. However, IEP patterns revolve around a common core of 11 components.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.418
Teacher spread0.352 · 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 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

Citations1
Published2019
Admission routes3
Has abstractyes

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