A Study of Information Literacy Initiatives between Secondary Schools and Universities in the UK
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".