Bibliographic record
Abstract
As we turn the corner on the first decade of the 21st century, debates about the future of the book and the library abound. While these are compelling questions, they are part of a larger phenomenon: technology is changing our information and reading behaviour. Literacy is no longer confined to the printed page; it is multi-modal. Learning in an electronic age presumes visual literacy, media literacy, and technological literacy. The electronic landscape challenges the conventions of traditional reading and what it means to be information literate. Academic subjects are targeting literacies that are specific to disciplinary knowledge that evoke deep understanding rather than superficial familiarity. Re-conceptualizing how we learn to read and write in print and electronic places and how we learn how to learn in new environments is the primary educational challenge.
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 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 | 0.028 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.061 |
| Scholarly communication | 0.055 | 0.073 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.039 | 0.024 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".