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Record W2605596914

Out with the Old: A Conversation about Canada’s Dated Special Education System

2016· article· en· W2605596914 on OpenAlexaffabout
Bianca Nucaro-Viteri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsTrent University
Fundersnot available
KeywordsSpecial educationInstitutionalisationAttendanceInclusion (mineral)IdeologySociologyPedagogyConversationIdentity (music)Political scienceGender studiesPublic relationsLawPoliticsAesthetics
DOInot available

Abstract

fetched live from OpenAlex

Canada’s education system is constantly evolving, with changes being made to policy and procedure on a consis- tent basis in order to accommodate for the growth of societal knowledge. Particularly, as society begins to accept a more intersectional framework, our education system should begin to reflect these changes. Special education classrooms are still a fairly common practice in the Canadian education system. The onset of special education was a response to the lack of education that was occurring for those with disabilities (Nor- wich, 2014). Shifts in ideology from institutionalization to special needs classrooms was initially beneficial to people with disabilities as it encouraged attendance in schools, as iter- ated by Dunlap the CEO of KIT (Kids Included Together) – an organization that offers free inclusion training (2015). Grad- ually, testing for various disabilities within primary schools increased and the label of “disabled” was embraced by the North American Education System. This naturally benefitted those within this community, as accommodations and indi- vidualized education plans were created. Conclusively, the onset of this model of education increased accessibility to education and intrinsically offered young students a concrete social identity, much like a sense of belonging to a minority group (Lauchlan & Boyle, 2007).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.610

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.266
Teacher spread0.256 · 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 designNot applicable
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

Citations0
Published2016
Admission routes2
Has abstractyes

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