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Record W1589867293

Sharing information literacy resources as open educational resources: lessons from DELILA

2012· article· en· W1589867293 on OpenAlexaboutno aff
Jane Secker, Natalia Madjarevic

Bibliographic record

VenueLondon School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2012
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsInformation literacyOpen educational resourcesCommonsInstitutionBest practiceLiteracyPublic relationsWorld Wide WebSociologyLibrary sciencePolitical scienceComputer sciencePedagogySocial science
DOInot available

Abstract

fetched live from OpenAlex

In all higher education institutions, librarians create a wealth of teaching resources that they use in their information literacy sessions. But is everyone reinventing the wheel, looking for the best way to teach search strategies, citing and referencing or keeping up to date for researchers? In practice librarians are usually more than happy to share their resources through numerous information literacy conferences and networks. Many of us share materials with colleagues across our own institution; some share more widely across institutions, for example, by using the UK’s learning resources repository Jorum (http:// www.jorum.ac.uk), either by putting material on open websites or even by emailing copies of our materials to colleagues. There have been several initiatives or projects to encourage librarians to share their teaching materials. One set of pages of the Information Literacy website (http://www. informationliteracy.org.uk) collects examples of good practice in teaching. In the USA and Canada the ANTS project (http://ants.wetpaint.com/) is a wiki where librarians can share library tutorials. However, it is only fairly recently that librarians have been encouraged to share their materials specifically as open educational resources (OERs), which means the materials have an open licence(such as Creative Commons).

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.011
Open science0.0040.004
Research integrity0.0000.001
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 head0.059
GPT teacher head0.397
Teacher spread0.338 · 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 designTheoretical or conceptual
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

Citations4
Published2012
Admission routes1
Has abstractyes

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