Bibliographic record
Abstract
This book questions the predominance of “media abundance” as a guiding concept for contemporary mediated politics. The authors argue that media abundance is not a universal condition, and that certain individuals, communities, and even nations can more accurately be referred to as media scarce – where access to media technologies and content is limited, highly controlled, or surveilled. Through case studies that focus on guerilla militants, incarcerated Indigenous people, and cold war‑era infrastructure, including Soviet “closed” or “secret” cities and Canadian nuclear bunkers, the book’s chapters interrogate how the once media scarce later “speak” to – and can be heard by – the predominant, abundant media culture. Drawing from several art projects and diverse cultural sites, the book highlights how media scarce communities negotiate and otherwise narrate their place in the world, their past experiences and lives, and escape from subjugation. To better understand media scarce politics, the book asks how and when communities become – by accident or force, by choice or necessity – media scarce. This innovative and insightful text will appeal to students and scholars around the world working in the areas of media and politics, art and politics, visual studies, surveillance studies, and communication studies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".