Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the challenges associated with identifying newspapers of record for local, regional and national newspapers, specifically as those challenges pertain to students’ news media literacy. Visual literacy and information literacy intersections are explored. Design/methodology/approach Newspapers of record for province/territory and state areas of Canada and the United States of America were identified for student project purposes. Criteria for newspaper of record qualification were investigated, refined, and applied to all newspapers reviewed. Findings Distinguishing newspapers of record based on traditional criteria is inadequate in an online environment. Criteria must be more flexible and address both the visual as well as the content aspects of newspapers. Neither database access nor native website access alone is sufficient for identifying these newspapers. Straightforward and definitive identification of these newspapers will no longer be possible. Practical implications Librarians will be faced with focusing on content or visual literacy, addressing both in a meaningful way during a single instruction session will be difficult. More strategic instruction within and across disciplines is necessary to produce news media-literate and savvy students. Originality/value News media literacy for students in all disciplines is an urgent need and must incorporate both visual and content literacies. In a time of proliferation of news sources, understanding the challenges associated with identifying newspapers of record for both librarians and students is a necessary step in this area of information literacy.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".