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Record W1683417281 · doi:10.29173/iasl7926

A Study of Information Literacy Initiatives between Secondary Schools and Universities in the UK

2021· article· en· W1683417281 on OpenAlexvenueno aff
Ray Lonsdale, Chris Armstrong

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
FundersJoint Information Systems Committee
KeywordsInformation literacyMedical educationData collectionPopulationLiteracyFace (sociological concept)Library sciencePsychologySociologyPedagogyMedicineComputer scienceSocial science

Abstract

fetched live from OpenAlex

This paper discusses the nature and conclusions of the second phase of a two-part project, CrossEd, undertaken between autumn 2004 and autumn 2005 and funded by the Joint Information Systems Committee in the UK. The study investigated collaborations taking place between secondary schools and universities in the provision of information literacy skilling relating to the use of e-resources. A survey of all university libraries in the UK was undertaken using an e-mail questionnaire to identify the incidence of current collaboration. The data from that survey provided information on the types of collaboration taking place. These were categorised and used to select the survey population of six university libraries for the qualitative study. Data collection was by means of face-to-face and telephone interviews with university librarians, using semistructured interview schedules. Six types of training for school pupils were identified, and the research revealed at least seven distinct positive aspects of cross-sectoral collaboration for school pupils. A fundamental lack of understanding of the respective roles of secondary school and university librarians was evident, and a range of issues to be addressed by librarians in both educational sectors was identified. A strategy for enhancing collaboration in the UK is also discussed.

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 score0.122
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.031
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 head0.023
GPT teacher head0.301
Teacher spread0.278 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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