Linguistic Bias in News Media on Anti-Pipeline Protests in Canada
Bibliographic record
Abstract
Protests give rise to social change, often advancing the rights of minority groups. However, their success hinges on public opinion, which can be influenced by news media (e.g., Detenber et al., 2007). Most research on this topic has demonstrated that protests that seek to disrupt the status quo are negatively framed in news media (e.g., Boyle et al., 2004), which inhibits their success. Others have found that the language used to describe minority groups in the news is often negatively biased (e.g., Dragojevic et al., 2017) and that this maintains harmful stereotypes (Beukeboom, 2014). However, no research has investigated the linguistic mechanisms used to describe protests in news media. Given this, the aim of this study was to determine whether news sources exhibit linguistic bias about protests that corresponds to regional differences, which are known to influence media bias. To do this, we examined articles published in news sources from different regions of Canada for linguistic bias in the description of Indigenous-led anti-pipeline protests. We found that the language used differed by region, such that news sources from the Prairies used more abstract language than news sources from the Central region. However, when considering both abstraction and valence, neither source exhibited a bias. This research is significant in that it demonstrates that regional differences in the way protests are framed extend to linguistic differences, but that negative framing may not.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".