Practicing Media—Mediating Practice | Reporting, Uncertainty, and the Orchestrated Fog of War: A Practice-Based Lens for Understanding Global Media Events
Bibliographic record
Abstract
Media coverage of the ongoing multistate conflict in Syria, extending into Iraq, has been punctuated by the marshaling of conditions for exceptional global media event coverage by states, citizens, and newsmakers alike. Where conditions for reporting are already limited because of ongoing conflict, both international agency coverage and governmental sources represent crucial conduits for dissemination of information worldwide. This research develops a practice-based lens to examine the embodied, geographic, and temporal networks of media production practices through a multilingual database comparison of French, Russian, American, and British newswire coverage of major military campaigns. Mapping shifts between on-the-ground reporting and dislocated geopolitical coverage against the temporal unfolding of event coverage reveals a new typology: the premediated media event.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.070 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".