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Record W1962161572 · doi:10.1111/1467-9604.12043

Paying the price for being inclusive: the story of Marshlands

2014· article· en· W1962161572 on OpenAlexaff
Jonathan Glazzard

Bibliographic record

VenueSupport for Learning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsMainstreamInclusion (mineral)SituatedSociologyPedagogyInjusticeSpecial educational needsPopulationPerformative utteranceSpecial educationPublic relationsPolitical scienceGender studiesPsychologySocial psychologyEpistemologyLaw

Abstract

fetched live from OpenAlex

This article illustrates, through the story of one mainstream primary school, the tensions between the inclusion agenda and the standards agenda. The school is situated in an area of social deprivation and nearly half of the school population have been identified as having special educational needs. The story presented in this article illustrates powerfully the inherent injustice of the performative culture which pervades education and the effects of this discourse for children with special educational needs and their teachers. I argue that a policy change is needed to create a more equitable education system and that, in the absence of such a change, schools such as the one presented here will risk being categorised as failing schools. This will have disastrous consequences for the teachers' careers, children's self‐concepts and the inclusion agenda itself.

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)0.007
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: none
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0240.029
Scholarly communication0.0100.013
Open science0.0010.012
Research integrity0.0050.014
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.327
Teacher spread0.318 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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