Then and now; themes in information literacy research in Anglophone countries from 2006-2016
Bibliographic record
Abstract
This paper addresses the questions: what were the major information literacy themes over the past decade (2006-16)? (RQ1); what is the current focus of IL research in Anglophone countries? (RQ2). The international conference LILAC (Librarians Information Literacy Annual Conference) was used as a lens through which to explore these developments. The paper seeks to reveal and analyse the major themes that have emerged from these events using qualitative content analysis to categorise findings. LILAC was chosen because: (1) it attracts contributions from around the world (30 countries in 2016) and consistently hosts contribution from the USA, Canada, Australia, New Zealand, Eire as well as the UK; (2) contributors are drawn from both information professionals as well as the academic research community. Findings show that the IL field is dominated by pedagogic research by librarians working in universities Higher Education to enable undergraduates and postgraduate become information literate. The focus of teaching and learning activities is largely on active learning and practicality or utility with expositions on how this can be achieved. Active learning is described in terms of constructivist and experiential approaches to teaching and learning as a means for maximising student engagement, especially through ‘hands-on’ interactivity using ICT or problem-solving.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".