Polarization in the Era of News Media Metamorphosis: A Critical Exploration of Implications for Democracy and Human Rights
Bibliographic record
Abstract
The foundations that sustain the public realm are crumbling as autocratic leaders seek to secure their privilege and undermine democracy. A cruel and bigoted ideology associated with USA President Donald Trump works to legitimize the cultural oppression of disabled, racialized, and queer people. Polarization is on the rise as democracy and human rights recede. The news media are a key pillar of democracy and a vigilant monitor of human rights. Rapid political, technological, and spatial change have rocked the news media landscape in recent years with possible implications for its ability to counter the assault on democracy. This study explored the influence of these changes on news media’s framing practices and the resulting contribution to polarization, democracy, and human rights in Canada and the USA during the era of Trump. Sampling identified a primary corpus of digital news media texts covering events related to sexual orientation and gender identity in schools. Content analysis of this initial corpus randomly identified a smaller sample of sixty digital media texts produced by three distinct news agency formations in each country. This secondary sample was then subjected to multimodal critical discourse analysis to illuminate the discursive framing practices and themes evident across the temporal period under study. To support this analysis, ChatGPT was utilized to access contextual information about yearly developments related to queer rights. The comparative analysis revealed that conflict frames inflame polarization in both countries, though this is tempered by other news media practices that reinforce democracy. Results illuminate a shift to pro-democracy framing practices in both countries in the wake of the spike of anti-democratic rhetoric associated with the 2020 electoral defeat of President Trump. They additionally indicate news media in both countries are both complicit and defiant to heteronormative attempts to suppress queer rights. The findings of this study contribute to wider discussions centred on the erosion of democracy and the complex interplay between the press, democracy, and systems of power, exclusion, and oppression. Recommendations hold particular relevance for journalists, educators, and all those who wish to resist the erosion of the democratic public realm.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.028 | 0.039 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".