Bibliographic record
Abstract
Free newspapers are a substantial segment of the U.S. newspaper industry, as well as an under-studied topic within media research. This study considers the economic health of free newspapers in the United States and whether they face a dire future given their heavy reliance on advertising, a source of revenue that has been in decline for newspapers. One question guiding this research is whether free newspapers face two options: continue producing free content by relying on advertising (in addition to other revenue sources), or abandon the advertising-based business model. Seven research questions address a number of issues, such as whether free newspapers are profitable, if decision-makers are considering changing their business model, whether they are seeking alternative sources of revenue, whether reader engagement is connected to the price, or a lack of one, of a newspaper, and whether decision-makers are optimistic or pessimistic about the future of their industry. A Web-based survey asked decision-makers at free newspapers in the United States to respond to questions related to the health and future of their newspaper or newspapers. This survey was complemented by in-depth interviews with publishers of four different types of free newspapers in Texas. The study concludes by suggesting free newspapers are not only viable but in many markets they are thriving. Sweeping generalizations (often seen in industry discourse) about the future of print newspapers can be misleading. This study contributes a reality check and calls for further research on the economics of print media in the digital era.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".