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Record W46776615 · doi:10.29173/iasl8212

Providing Potential for Progress: Learning Support for Students with Special Educational Needs

2021· article· en· W46776615 on OpenAlexvenueno aff
Margaret Kinnell Evans, Peggy Heeks

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
FundersLoughborough University
KeywordsStaffingSpecial educational needsSubject (documents)Work (physics)Special needsSpecial educationNeeds assessmentMedical educationPedagogyMathematics educationPsychologyLibrary scienceSociologyEngineeringMedicineComputer scienceNursingSocial science

Abstract

fetched live from OpenAlex

This paper considers the purposes, methods, findings and significance of the British Library LESSEN (Learning Support for Special Educational Needs) Project. The focus was on Year 7 students, i.e., in their first year of secondary education (aged 11-12 years), in ten English secondary schools who were on the Special Educational Needs (SEN) register because of their learning difficulties. Case studies were undertaken in 10 schools located in five Local Education Authorities (LEAs). Data were collected from documents, observation and an extensive interview program, both within schools and with LEA and schools library services staff. Work with individual children was also undertaken, supporting in subject lessons and in the SEN base, as well as assisting in the library, to provide an action research element to the investigation. Varying levels of library and staffing were found and recommendations were made as to future progress. The project report, Learning support for special educational needs is due for publication in 1997 by Taylor Graham.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
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 head0.028
GPT teacher head0.343
Teacher spread0.314 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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