Bibliographic record
Abstract
Scholarship and literacies in a digital age There is growing interest in the impact of digital technologies on meaning-making practices and identity in education, which has been explored via the related concepts of ‘digital scholarship ’ and ‘digital literacies’. However, to date, much published work in this area has been descriptive, identifying possibilities or promoting specific kinds of intervention. Far rarer is work that develops concepts, links to research outside of the field of learning technology or work that uses new data to challenge existing orthodoxies. Substantial effort has been invested in developing and promoting digital literacy. It has been the focus of European projects (Martin and Grudziecki 2006); became the cornerstone for substantial investment in the United Kingdom by the JISC (Beetham, McGill and Littlejohn 2009), leading to 12 projects being funded1; became the focus for an The Economic and Social Research Council (ESRC) seminar series,2 which led to a book being produced (Goodfellow and Lea 2013); and has stimulated international networks such as the UK-Canadian programme of workshops on ‘New
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.216 | 0.095 |
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".