Bibliographic record
Abstract
Newspapers once were required reading for individuals who wished to gain new information about the world around them. Information behaviour scholars linked newspaper reading to passive acts of acquiring and acclimating new information that later helped individuals expand their perspective, solve problems, and feel a sense of connection with their community. However, shifting information consumption habits, digital communication technology, and the steady growth of capitalistic news conglomerates have led to thousands of newspapers closing since the dawn of the 21st century. Many small and rural communities have lost their only news outlet. Research on the closures of newspapers has raised concerns about the political and social implications. At the same time, newspapers have been accused of legitimizing power structures and social hierarchy, and some see their demise as an opportunity to create new information structures that better reflect their communities. Libraries may be able to play a role in filling the information void that is created when a newspaper closes in a small community. Still, there is a need for broader research to understand the wider implications of newspaper closures and the role libraries can play in the wake of their closures.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".