Beyond the headlines: Media and Information Literacy (MIL) in times of conflict
Bibliographic record
Abstract
The wars of the 21st century are not the first media wars, and many tropes and schema have long histories, particularly propaganda and the othering of a purported enemy. What is new today is that although mass media remains a central and hegemonic source of insight and perspective, citizen journalism, social media, spreadable media, and surveillant, data-driven media have grown in significance at an exponential level, adding a layer of complexity. In this article, we focus on disparity in media coverage and make the point that media and information literacy provide a valuable set of lenses from which to view a cluster of news and social media accounts taken from the government, mainstream media, alternative media, and the DIY mediasphere of the social media. It centers on two conflicts that receive little media exposure -the Nagorno-Karabash conflict between Armenia and Azerbaijan and the internal Anglo-Francophone conflict in Cameroon. It also offers examples of classroom activities that could be adapted and modified to most educational settings.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".