Israel's Strategic Ontology of Victimhood in News Media Coverage of the 2024 Maccabi Tel Aviv Football Violence in Amsterdam
Bibliographic record
Abstract
Dataset Description:This dataset was created for the study Israel’s Strategic Ontology of Victimhood in News Media Coverage of the 2024 Maccabi Tel Aviv Football Violence in Amsterdam. It relates to how Western European and North American English-language media framed the violence surrounding the November 7, 2024, football match between Ajax Amsterdam and Maccabi Tel Aviv. Articles were retrieved from the Nexis Uni and ProQuest databases using the keywords Israel and Amsterdam for the period November 6–20, 2024. The geographical scope included Europe and North America. A total of 464 articles from approximately 142 unique news sources were collected, though some sources (e.g., BBC, CNN, Wall Street Journal) were heavily overrepresented. The data collection represents a census of indexed English-language news coverage within the search parameters. The dataset contains two major components: Framing Analysis: Each article was coded for its primary frame (Israeli Victimhood, Non-Israeli Victimhood, or Mutual Aggression) and up to five thematic sub-frames based on a developed dictionary. The coding prioritized the first 10 words of the article's headline and lead paragraph. Source Analysis: The first ten sources cited per article were documented along with their institutional affiliation and the stance taken (whether the source emphasized Israeli victimhood or acknowledged alternative perspectives). AI-assisted qualitative coding was conducted using OpenAI’s GPT-4 model, supplemented with manual intercoder reliability testing (Cohen’s Kappa ≈ 0.7). Key Metadata: Timeframe Covered: November 6–20, 2024 Source Databases: Nexis Uni, ProQuest Regions Covered: Europe, North America Number of Articles: 464 articles analyzed; 452 usable for final coding Number of News Sources: Approximately 142 Dominant Outlets: BBC, CNN, Wall Street Journal, New York Times, Daily Mail, Guardian, GB News, Canadian Press, AFP, DPA Primary Frames: Israeli Victimhood (IV), Non-Israeli Victimhood (NI), Mutual Aggression (MA) Sub-frames: 33 distinct sub-frames identified Intercoder Reliability: 90% overlap on sample manual coding (Cohen's Kappa ~ 0.7) Tools Used: OpenAI GPT-4 for content coding; manual verification Use and Access:This dataset is intended for researchers examining media framing, strategic ontologies, international conflict reporting, and political communication. It can support studies in journalism studies, media sociology, Middle East politics, conflict studies, and AI-assisted content analysis.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".