Framing economic inequality in the news in Canada and the United States
Bibliographic record
Abstract
Abstract News frames are the interpretations and emphases in the presentation of complex issues that privilege certain understandings over others. We take the case of the framing of economic inequality in the news in order to make an empirical contribution and a conceptual contribution. The framing of economic inequality in news coverage is important to understand as news frames can shape public opinion and subsequently policy responses to inequality. There is no research we are aware of that documents how economic inequality is framed in the news. We compiled a dataset of 2109 news articles, published between 2000 and 2014, about economic inequality, and conducted a detailed content analysis. Empirically, we document how inequality was framed as an issue, specifically whether it was framed as a social problem with negative consequences, and what its causes, consequences, and solutions were. Conceptually, we also address an important gap in knowledge about the determinants of news frames. Research about how news frames are shaped by contextual factors in the production of news is uncertain about which factors matter and to what extent. The dataset has a set of features that allow us to simultaneously examine a number of potential determinants of news frames. Our data allow us to compare the relative influences on news coverage of economic inequality of (1) national context, (2) the political leaning of newspapers, (3) changing economic conditions, and (4) social movement efforts. Of these four factors, we find that only the Occupy movement influenced the volume of attention and the identification of economic inequality as a problem with negative consequences. National context, political leaning, and change in economic conditions had much more limited, or no, influence. Following the emergence of the Occupy movement, attention to economic inequality increased and remained higher than before. However, despite the clear effects of the Occupy movement on problem identification, news coverage of the causes and solutions to economic inequality did not significantly shift. We, therefore, find that social movement activity had the clearest influence on news frames, but the observed effect in this case was superficial rather than detailed.
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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".