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Record W4229064722 · doi:10.1111/bld.12474

Fifty years of the British journal of learning disabilities: The power of the past

2022· article· en· W4229064722 on OpenAlexaff
Ian M. Davies, Edurne García Iriarte, Simon Jarrett, Kelley Johnson, Tim Stainton, Elizabeth Tilley, Jan Walmsley

Bibliographic record

VenueBritish Journal of Learning Disabilities · 2022
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLearning disabilityPsychologyPower (physics)Developmental psychology

Abstract

fetched live from OpenAlex

Accessible summary This special issue of the journal celebrates the 50th birthday of the British Journal of Learning Disabilities ( BJLD ). The stories in this special issue are about the history of learning disability from around the world. It is important that people know about the history of learning disability, because people with learning disabilities have been kept in the dark for too long. Ian Davies says “why should we be forgotten? We're as much a part of society as everyone else”. Over the years BJLD has included stories about learning disability history, but many of these stories were written by people who do not have learning disabilities. In the past few years, people with learning disabilities have been doing important history projects. In this special issue, we have included stories about history that have been written by people with learning disabilities. In the next 50 years, it needs to be easier for people with learning disabilities to write for journals like BJLD . It should also be easier for people with learning disabilities to find out about other people's research in journals like BJLD .

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)0.005
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient 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 score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.022
GPT teacher head0.277
Teacher spread0.255 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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