The Representation of Muslims in Public Spot Advertisements Against Islamophobia: The Case of USA, Canada and the Netherlands
Bibliographic record
Abstract
In recent years, it has been seen that the extreme right-wing politicalparties in the world are frequently the subject of Islamophobia in theirpropaganda activities. These parties reflect Islam as a danger in theirpropaganda. Propaganda has led to the emergence of discrimination againstMuslims over time. In order to eliminate discrimination against Muslims, publicspot advertisements have been prepared in international arena againstIslamophobia. It was aimed to eliminate the negative propaganda myths builtagainst Muslims in public spot advertisements. In this study, how and in whatway Muslims are represented in public spot advertisements prepared against theinternational arena against Islamophobia. In the study, it was also tried tofind out how the criticism of the propaganda myths built for Muslims wascriticized. For this purpose, three public spot advertisements, which weredetermined by using the sampling method among the anti-Islamophobia public spotadvertisements, which have recently been effective in the international arena, wereexamined in the semiotic analysis method in the qualitative research methods.Public spot ads were analyzed within the framework of the semiotic approach ofFrench Linguist Roland Barthes. According to the findings, it was stated that Muslimshave been discriminated in the societies where the they live with Islamophobiain public spot ads. On the other hand, it is aimed to emphasize that in publicspot advertisements, Muslims are part of the society in which they are locatedand so it was tried to eliminate discrimination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".