BLACK LIVES MATTER Krossade rutor eller fredliga demonstrationer? - En kvantitativ innehållsanalys av svenska nyhetstidningars porträttering av Black Lives Matter och demonstrationerna till stöd för rörelsen.
Bibliographic record
Abstract
The purpose of this study is to examine how five Swedish newspapers portrayed the Black Lives\nMatter (BLM) movement and supporting demonstrations during a time period between May 25th and June 14th, 2020.\n\nThe theory of framing, how people’s perception of a subject is affected by the media portrayal, is the\nmotivation behind this study. Theories about common news values and news angles are applied to\nthe articles. Additionally, quoted sources and descriptions of violence are investigated to determine if a protest paradigm, a pattern of news coverage that tends to focus on violence and disruption rather than the reason behind the demonstration, can be found in Swedish newspapers.\n\nThe study uses a quantitative content analysis of 266 articles about the Black Lives Matter movement\npublished by the newspapers Aftonbladet, Dagens Nyheter, Expressen, Göteborgs-Posten and Sydsvenskan. During the analyzed time period, major demonstrations were held in Sweden’s three\nlargest cities: Stockholm, Gothenburg and Malmö. With a cumulative approach, the theoretical\nframework has been operationalized to create a codebook for analyzing the articles.\n\nAccording to previous research, there are many signs of a protest paradigm in American newspapers.\nThis pattern was similarly present in the Swedish articles, evident in their recurrent use of official\nsources like politicians and police. However, in the analysis of all the articles about BLM, the\nprotesters were quoted more often than police. While in the articles specifically about demonstrations\nin Sweden, protesters were more likely to be excluded. For example, protesters were more often\nmentioned than quoted compared to the police sources. This study confirms signs of the protest\nparadigm in the articles about the demonstrations, evident in the great focus on violence and\ndisruption and the lack of protester voices. Unlike previous research, all protesters are not blamed for\nunlawful activity that occurred during the demonstrations. Swedish newspapers tend to carefully\ndistinguish provocators from peaceful protesters. Finally, the covid-19 pandemic affected the media's\nportrayal of BLM and the demonstrations. The global pandemic was frequently mentioned and often\nused as a news angle in the articles.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".