Analysis of Scientific Publications on the Gaza-Israeli Conflict.
Bibliographic record
Abstract
BACKGROUND: The Gaza-Israeli conflict poses challenges for unbiased reporting due to its complexity and media bias. We explored recent scientific publications to understand scholarly discourse and potential biases surrounding this longstanding geopolitical issue. OBJECTIVES: To conduct a descriptive bibliometric analysis of PubMed articles regarding the recent Gaza-Israeli conflict. METHODS: We reviewed 1628 publications using keywords and medical subject headings (MeSH) terms related to Gaza, Hamas, and Israel. We focused on articles written in English. A team of researchers assessed inclusion criteria, resolving disagreements through a third researcher. RESULTS: Among 37 publications, Lancet, BMJ, and Nature were prominent journals. Authors from 12 countries contributed, with variety of publication types (46% correspondence, 32% news). Pro-Gaza perspectives dominated (43.2%), surpassing pro-Israel (21.6%) and neutral (35.1%) viewpoints. Pro-Gaza articles exhibited higher Altmetric scores, indicating increased social media impact. Pro-Israel publications were predominantly authored by Israelis. CONCLUSIONS: The prevalence of pro-Gaza perspectives underscores challenges in maintaining impartiality. Higher social media impact for pro-Gaza publications emphasizes the need for nuanced examination. Addressing bias is crucial for a comprehensive understanding of this complex conflict and promoting balanced reporting.
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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 | 0.030 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.143 | 0.156 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".