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Record W2127724067

Principles, Policies, and Practices in Special Education in British Columbia.

2001· article· en· W2127724067 on OpenAlexvenueaboutno aff
Nancy E. Perry, John K. McNamara, K. Louise Mercer

Bibliographic record

VenueExceptionality education Canada · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsnot available
Fundersnot available
KeywordsSpecial educationTribunalChristian ministryIntervention (counseling)Political scienceBest practiceSupreme courtPublic administrationState (computer science)Education ActLawSociologyPublic relationsPedagogyMedicineNursing
DOInot available

Abstract

fetched live from OpenAlex

In this article, we examine the state of special education services in British Columbia for students with learning disabilities (LDs) against the backdrop of a legal challenge that currently has BC's Ministry of Education defending its policies and procedures. Specifically, we relate BC's current policies and practices regarding the provision of services to students with LDs to principles of best practice that are drawn from theory and research about definitional issues, early intervention, and resource allocation. The results of our examination indicate that policy documents authored by BC's Ministry of Education reflect much of what is considered to be best practice for students with LDs. However, the ministry needs to do more to ensure that their policies concerning early intervention, personnel, and programs are enacted more consistently in schools across the province. Also, they need to increase funding for students with high incidence disabilities in general, and LDs in particular. These findings are echoed in the rulings of both the Human Rights Tribunal and the Supreme Court of BC.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.931
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.009
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.026
GPT teacher head0.263
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2001
Admission routes2
Has abstractyes

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