Digital Curation as a Core Competency in Current Learning and Literacy: A Higher Education Perspective
Bibliographic record
Abstract
Digital curation may be regarded as a core competency in higher education since it contributes to establishing a sense of metaliteracy (an essential requirement for optimally functioning in a modern media environment) among students. Digital curation is gradually finding its way into higher education curricula aimed at fostering social media literacies. Teachers are urged to blend informal and formal learning and since most people informally use curation in their daily lives for compiling relevant information, it may be fairly easy to adopt digital curation in teaching and learning. Teachers, however, require considerable insight in incorporating various informal digital curation tools in educational practices. The SECTIONS model may assist in guiding decisions around the suitability of digital curation tools for a higher education environment. Including digital literacy training in the professional development of academic staff members may sensitize them to the possibilities that incorporating digital approaches in curricula offer. The Five Cs of Digital Curation framework may guide academic staff members in compiling suitable digital material. There as yet appears not to be a pedagogy that fully acknowledges the various digital curation processes. A pedagogy of abundance, acknowledging that content often is freely available and abundant, may eventually prove relevant in this regard.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".