Bibliographic record
Abstract
Local media is at a turning point. Legacy outlets – television and newspapers – are declining while emerging platforms are failing to take their place. When it comes to the policies and regulations governing local television, regulators are struggling to address audience gravitation and fragmentation, the declining commercial viability of broadcasting, and the ongoing crisis of journalism. In an era of digital platforms such as YouTube and Facebook, regulators are also grappling with a question they had never anticipated: What does it mean to be local in the digital age? The lack of an answer has left them unsure of how to define a locality, what counts as local news, if the information needs of communities are being met, and the larger role of local media in a democracy. Through comparative analysis, Media Localism explains, assesses, and critiques these issues and asks how communication regulators in the United States, Canada and the United Kingdom defined, mobilized and regulated “the local” in broadcasting from 2000 to 2012. Using critical theories of space and place, critical regionalism and critical political economy, and based on document analysis and interviews, Ali offers a fresh approach to localism in media policy. Through policy critique and intervention Ali argues that it is only through redefining the scope of localism that regulators can properly understand and encourage local media in the 21 st century.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".