Bibliographic record
Abstract
Particularly in coverage of unresolved conflicts, mass media news of both the one-to-many broadcast era and the new networked era are not guaranteed to sufficiently provide the historical and analytical depth required for publics to understand these infinitely complex tensions in their respective cultural and temporal context. Mass media news coverage of the day, however, does perpetuate mediated images that seek to affect how publics contextualize and collectively remember simmering cultural conflicts into the future, afar and close to home. This article conducts a small-scale theoretical review of the theories of collective memory and agenda-setting, complemented by an analysis of mass media news coverage and literature on unresolved conflicts concerning the Front de libération du Québec (FLQ) and “The Troubles” in Northern Ireland. This interrogation, in light of theoretical conceptions of dominant news discourses, offers an explanation as to how publics may come to understand ongoing conflicts in the external world. In shaping understandings of unresolved conflicts by publics, mass media news can play a biased role in making certain political tensions affectively salient for the preservation of a nation’s collective past by attempting to influence how compassion is evoked from publics in the present and even into the future. While certainly historically and geopolitically situated, a commonality exists between the unresolved conflicts of the FLQ terrorist attacks in Québec, Canada and the Troubles in Northern Ireland: nationalist cultural tensions, ones that simmer cyclically until discontent erupts between players on opposing sides. Although apparent collectives can seek out countless contemporary alternative sources of information in the digital era of abundance, this paper argues that collective memory remains significant in the context of scrutinizing how mass media news problematically sets biased agendas, which then promotes quite different historical worldviews of deeply complex conflicts.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.014 | 0.037 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".