Bibliographic record
Abstract
Literacy, until recently, was defined as the ability to read printed text and to understand the nuances of both the form and content of that printed text. More recently there has been a focus on subsets of literacy – data literacy, numeracy, visual literacy, media literacy, etc. – that recognizes the means of communicating ideas and facts are not limited to the printed text and that there are multiple means which may be more powerful ways of communicating in our world. In recent years, higher education has been redefining what it means to be educated – from a focus on specific bodies of knowledge, or disciplines, to a focus on developing and mastering skills for varying modes of inquiry. Simultaneously, there has been a growing focus on expanding how students and faculty communicate knowledge – what was once strictly the term paper approach is being replaced by the oral presentation, the poster session, or the artistic response. In a world where ideas are more readily communicated via social media such as YouTube, Instagram, Facebook and Twitter, the ability to accurately assess additional modes of communication is critical. This paper will explore different subsets of literacy, describe a method for developing mastery of those literacies in higher education, and advocate for academic library professionals to become specialists focused on literacies as much, if not more, than on content.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".