Old Media, New Media, and the Complex Story of Disasters
Bibliographic record
Abstract
Abstract Media have always played an important role in times of emergency and disaster. Undersea cables, international news agencies, the press, radio and television, and, most recently, digital and mobile technologies—all have played myriad and complex roles in supporting emergency response and notification, and in helping constitute a shared experience that can be important to social mobilization and community formation. The geographical location of disasters and the identities of victims, the increasingly visual nature of disaster events, and the ubiquitous nature of media in our lives, all shape and influence which kinds of emergencies attract global media and public attention, and how we come to understand them. Globalization has compressed time and space such that a whole range of disasters—from natural events (cyclones, earthquakes, and hurricanes) to industrial accidents and terrorist attacks—appear on our television and mobile screens with almost daily frequency. There is nothing inherent about these events that give them meaning—they occur in a real, material world; but for many of us, our experience of these events is shaped and determined in large part by our interactions with media industries, institutions, and technologies. Understanding the media’s construction of these events as disasters provides important insight into the nature of disaster mitigation, response and recovery.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".