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Record W1860705329

Alternative Education Provision at Key Stage 4

2000· book· en· W1860705329 on OpenAlexaboutno aff
Mairi Ann Cullen, Felicity Fletcher‐Campbell, Ella M. Bowen, Jayne Osgood, Sarah A. Kelleher

Bibliographic record

VenueOpenGrey (Institut de l'Information Scientifique et Technique) · 2000
Typebook
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsTruancyCurriculumWork (physics)Quarter (Canadian coin)Political sciencePedagogyPublic relationsMedical educationPsychologyGeographyMedicineEngineering
DOInot available

Abstract

fetched live from OpenAlex

Disaffection, lack of interest, truancy and under-achievement are all too common a reaction to the curriculum in years 10 and 11. But many schools across England and Wales have chosen to offer at least some of their students something a little different during their last year or two of compulsory education. For example, as well as attending school for part of the time, some students go to further education college, gain experience on a long-term work placement, learn new skills with a training organisation or get involved in personal-development activities run by youth workers. In the summer term of 1998, NFER carried out a survey of schools in a sample of LEAs, asking them what alternative curriculum programmes they used for some students at key stage 4. Just under a quarter of the schools contacted told us about the schemes they had in place. During autumn term 1998, representatives from 75 of the organisations working with schools were also interviewed about their perspectives on these programmes. These partner organisations included LEAs, LEA support services, youth work, community colleges, other schools, employers, further education colleges, training and enterprise councils, training organisations, and special projects run in particular geographic areas.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.004
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.029
GPT teacher head0.341
Teacher spread0.312 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2000
Admission routes1
Has abstractyes

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