\documentclass[11pt]{article}
\usepackage[utf8]{inputenc}
\usepackage{geometry}
\usepackage{xcolor}
\usepackage{hyperref}
\usepackage{fancyhdr}
\usepackage{titlesec}

\geometry{margin=1in}
\setlength{\parindent}{0pt}
\setlength{\parskip}{0.8em}

% Header and footer
\pagestyle{fancy}
\fancyhf{}
\fancyhead[L]{Review Report}
\fancyhead[R]{Generated by AI Scholar Frontier}
\fancyfoot[C]{\thepage}

\title{\textbf{Empirical Communication and Trust Construction in ACLED Data of the 2023-2024 Palestine–Israel Conflict}}
\author{}
\date{}

\begin{document}

\maketitle

\begin{center}
\textit{Generated by AI Scholar Frontier}
\end{center}

\vspace{1em}

\section*{Paper Information}

\textbf{Paper Title:} Empirical Communication and Trust Construction in ACLED Data of the 2023-2024 Palestine–Israel Conflict

\textbf{Review ID:} review_20251127_213918

\textbf{Generated:} 2026-06-07 10:24:11

\vspace{1em}

\hrule

\section{AI Reviewer 1 - Comprehensive Review}

**Review of "EMPIRICAL COMMUNICATION AND TRUST CONSTRUCTION IN ACLED DATA OF THE 2023-2024 PALESTINE–ISRAEL CONFLICT"**

---

\textbackslash{}#\textbackslash{}#\textbackslash{}# **🔍 Step 1. Summary of the Paper**

This manuscript employs a mixed-methods approach to analyze the ACLED dataset documenting the 2023–2024 Palestine–Israel conflict. The authors investigate how trust and credibility are constructed in conflict data under conditions of information asymmetry and restricted access. The study integrates quantitative analysis of 7,420 conflict events (including regional distributions, event types, and fatalities) with qualitative thematic coding of narrative fields. Key findings include: the Gaza Strip experienced 43.3\textbackslash{}% of recorded events with disproportionately high fatality rates (mean: 5.57 per event); air/artillery strikes were the deadliest event type (mean fatalities: 6.1); and qualitative analysis identified frequent references to children (28.4\textbackslash{}% of entries), hospitals (24.0\textbackslash{}%), and schools (17.8\textbackslash{}%), framing the conflict in humanitarian terms. The paper claims to contribute to understanding epistemic trust, moral witnessing, and community-based documentation as ethical infrastructure in conflict reporting.

---

\textbackslash{}#\textbackslash{}#\textbackslash{}# **🔬 Step 2. Evaluation Criteria**

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **1. Originality / Novelty**  
**Score: 6/10**  
The integration of quantitative ACLED data with qualitative narrative analysis in conflict zones is not entirely novel, but applying this to the 2023–2024 Palestine–Israel conflict with a focus on trust construction adds contemporary relevance. The theoretical framing around epistemic justice and moral witnessing is derived from established literature (e.g., references to oral history, decolonial theory). While the mixed-methods approach is commendable, it does not substantially advance methodological innovation. The paper’s originality lies in its empirical focus on a specific, high-profile conflict, but it falls short of introducing new theoretical or analytical frameworks.

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **2. Scientific Rigor / Methodology**  
**Score: 5/10**  
- **Research Design:** The concurrent mixed-methods design is appropriate, but the reliance on secondary data (ACLED) without primary validation (e.g., interviews with data collectors) limits depth. The sampling strategy for qualitative analysis (systematic examination of all 7,420 entries) is thorough but may lack prioritization of high-information narratives.  
- **Flaws and Biases:** The study does not adequately address potential biases in ACLED data collection (e.g., sourcing limitations, verification challenges in conflict zones). The quantitative analysis relies heavily on descriptive statistics (frequencies, means, Pearson correlations), but inferential statistics or regression models are absent, weakening causal claims. The qualitative coding process, while detailed, lacks inter-coder reliability measures.  
- **Ethical Approval:** The use of publicly available, anonymized data is ethically sound, but the paper does not explicitly state compliance with institutional review board (IRB) standards for secondary data analysis.

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **3. Clarity \textbackslash{}& Presentation**  
**Score: 7/10**  
The paper is generally well-structured, with clear sections and logical flow. However, the writing is occasionally dense and jargon-heavy (e.g., "epistemic resistance," "data-mediated witnessing"), which may hinder accessibility. Tables are informative but could be better integrated with the text (e.g., explaining implications of regional disparities in Table 2). The abstract and conclusions accurately reflect the findings but overstate theoretical contributions (e.g., "development of methodological approaches" is vague). Figures are absent; visualizations of temporal trends or thematic networks would enhance clarity.

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **4. Reproducibility \textbackslash{}& Transparency**  
**Score: 4/10**  
- **Methods:** The description of quantitative and qualitative procedures is detailed but incomplete. For example, the qualitative coding framework (e.g., codebook, decision rules for thematic development) is not provided. The handling of missing data ("listwise deletion") is mentioned but not justified.  
- **Data/Code Availability:** No mention of data or code sharing. ACLED data is publicly available, but the processed dataset (e.g., merged quantitative-qualitative data) and analysis scripts are not referenced.  
- **Statistical Robustness:** Pearson correlations are reported for actor interactions, but assumptions (e.g., linearity, normality) are not checked. The civilian fatality age estimates (Table 5) are described as "estimated from reports" without methodological transparency (e.g., estimation techniques, uncertainty ranges).

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **5. Significance \textbackslash{}& Impact**  
**Score: 7/10**  
The paper addresses an important problem: trust in conflict data amid information asymmetry and humanitarian crises. The focus on the 2023–2024 Palestine–Israel conflict ensures topical relevance, and the findings could inform humanitarian policy, data ethics, and conflict reporting. However, the impact is limited by methodological constraints (e.g., secondary data, lack of primary insights). Experts in conflict studies or humanitarian informatics may find the mixed-methods approach useful, but the paper does not offer field-changing insights.

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **6. Ethics \textbackslash{}& Integrity**  
**Score: 8/10**  
The authors demonstrate ethical reflexivity by acknowledging power dynamics in knowledge production, secondary data limitations, and the traumatic nature of conflict documentation. No evidence of plagiarism or data manipulation. However, the paper could more explicitly discuss potential biases in ACLED data (e.g., underreporting, political influences) and the researchers’ positionality (e.g., how their perspectives shaped interpretation). Conflicts of interest are not mentioned but are unlikely given the anonymous authorship.

---

\textbackslash{}#\textbackslash{}#\textbackslash{}# **🧪 Step 3. Specific Suggestions for Improvement**

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **Major Flaws to Address:**  
1. **Methodological Rigor:**  
   - Incorporate inferential statistics (e.g., regression models) to test relationships between variables (e.g., event types and fatalities).  
   - Address ACLED data limitations explicitly: discuss sourcing biases, verification processes, and how missing data may affect findings.  
   - For qualitative analysis, report inter-coder reliability metrics and provide a codebook in supplementary materials.  
2. **Theoretical Contribution:**  
   - Clarify how the study advances epistemic trust or moral witnessing theory beyond applying existing frameworks.  
   - Differentiate findings from prior mixed-methods conflict studies (e.g., cite specific gaps in literature).  
3. **Reproducibility:**  
   - Share processed datasets and analysis scripts in a repository.  
   - Detail qualitative coding procedures (e.g., examples of open/axial coding, theme development).  

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **Minor Flaws:**  
- Reduce jargon and improve readability for interdisciplinary audiences.  
- Include visualizations (e.g., time-series plots, thematic maps) to complement tables.  
- Fix formatting inconsistencies (e.g., hyphenation in the title, undefined acronyms like "ACLED" in abstract).  
- Proofread for minor typos (e.g., "constrUC - TION" in title).  

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **Additional Experiments/Analyses:**  
- Conduct sensitivity analyses for civilian fatality estimates (e.g., bootstrap confidence intervals).  
- Compare ACLED data with other sources (e.g., UN reports, local NGOs) to triangulate findings.  
- Add a case study of specific events to illustrate qualitative themes in depth.  

---

\textbackslash{}#\textbackslash{}#\textbackslash{}# **📊 Step 4. Final Decision \textbackslash{}& Justification**

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **Overall Score: 6/10**  
The paper presents a timely analysis of trust in conflict data using a mixed-methods approach, with strengths in ethical reflexivity and integration of quantitative-qualitative evidence. However, methodological limitations (e.g., reliance on descriptive statistics, lack of primary data) and modest theoretical contributions reduce its rigor and impact. The study would benefit from deeper analytical methods and greater transparency.

\textbackslash{}#\textbackslash{}#\textbackslash{}#\textbackslash{}# **Recommendation: Borderline**  
**Justification:** While the paper addresses an important topic and employs a relevant methodological framework, it falls short of the novelty and rigor expected for a high-impact journal. The findings are descriptive rather than explanatory, and the lack of primary data or advanced statistical analysis limits its contribution. With major revisions (e.g., robust statistical testing, addressing data biases, enhancing reproducibility), the manuscript could meet publication standards. However, in its current form, it is not suitable for acceptance.

\newpage

\section{AI Reviewer 2 - Critical Review}

**Reviewer 02 Assessment**

**1. Overall Impression**
My immediate reaction is one of significant methodological and conceptual concern. The manuscript presents an ambitious mixed-methods study on a highly relevant and sensitive topic. However, it reads more as an advocacy piece framed in academic language than a rigorous, impartial scientific investigation. The theoretical framing around "epistemic injustice" and "moral witnessing" appears to predetermine the conclusions, creating a strong confirmation bias throughout the analysis. The study feels overhyped, with claims of "ethical infrastructure" and "epistemic resistance" far outstripping the evidence provided. The core weakness is the failure to treat the ACLED dataset as a variable construct to be critically evaluated, instead treating it as a neutral ground truth from which to analyze "trust construction."

**2. Technical \textbackslash{}& Scientific Assessment**

**A. Problem Definition: 2/5**
*   The research questions are clearly stated but are fundamentally biased. Questions like "How does institutional framing shape moral authority..." assume the conclusion that institutional framing *does* shape moral authority in a particular way. The problem is framed entirely from a single theoretical perspective (decolonial/epistemic injustice), ignoring alternative explanations for patterns in the data. The motivation is politically charged rather than scientifically neutral.

**B. Methodological Soundness: 1/5**
*   **Fatal Flaw in Causal Logic:** The study claims to investigate "how trust and credibility are constructed" but uses the ACLED data *as its primary source*. This is circular reasoning. It assumes the data is credible to analyze how credibility is constructed. A sound methodology would require independent verification of events or a comparative analysis with other datasets (e.g., UN OCHA, B'Tselem, IDF data) to actually assess credibility.
*   **Statistical Overreach:** The "estimation" of civilian age distribution (Table 5) is methodologically indefensible. The paper cites "established methodological frameworks" but provides no detail on the estimation model, its assumptions, or validation. Deriving a precise percentage (42\textbackslash{}%) from qualitative narrative fields is statistically dubious and highly misleading.
*   **Qualitative Analysis as Quantification:** The "qualitative" analysis is largely reduced to keyword counting (Table 6). This misses the entire point of qualitative inquiry, which is to understand meaning, context, and nuance. The rich, interpretive potential of narrative analysis is lost in a simplistic frequency count that is then used to make sweeping claims about "humanitarian framing."

**C. Results \textbackslash{}& Evidence: 1/5**
*   **Lack of Baselines and Controls:** The paper presents descriptive statistics (events, fatalities) but provides no context for interpreting them. What were the baseline rates before the conflict? How do these figures compare to other conflicts? Without this, claims of "disproportionate impact" or "asymmetric" violence are unsubstantiated.
*   **Exaggerated Claims:** The claim that "credibility emerges through consistent documentation practices" is not proven. Consistency is demonstrated, but credibility requires external validation, which is absent. The finding that mentions of "children" and "hospitals" are common is presented as evidence of "moral witnessing" rather than simply reflecting the realities of urban warfare or the specific coding priorities of ACLED and its sources.
*   **Reproducibility:** While the dataset is named, the specific procedures for the qualitative "thematic analysis" are too vague to be reproducible. The process of moving from keywords to overarching themes like "epistemic resistance" is not transparent.

**D. Contribution to the Field: 2/5**
*   The topic is important, but the contribution is severely undermined by methodological flaws. It does not advance methodological knowledge, as its mixed-methods approach is poorly executed. Its theoretical contribution is a restatement of existing decolonial and epistemic injustice frameworks applied to a new dataset, but without the rigorous evidence required to support the application.

**E. Writing \textbackslash{}& Presentation: 3/5**
*   The paper is well-structured and written in a fluent academic style. However, this polished presentation masks the underlying conceptual weaknesses. The use of theory is decorative rather than integral to a rigorous analytical framework.

**F. Ethical \textbackslash{}& Transparency Standards: 2/5**
*   The use of secondary, anonymized data likely meets basic ethical standards. However, there is a significant ethical concern regarding the presentation of speculative demographic estimates (Table 5) as factual results. The lack of transparency about the estimation methodology violates core principles of research integrity. The strong, pre-determined theoretical stance also raises questions about interpretive objectivity.

**3. Strengths**
*   Addresses a timely and critically important subject.
*   Attempts a complex mixed-methods design, which is laudable in ambition.
*   Compiles descriptive statistics from the ACLED dataset for a specific period, which has some archival value.

**4. Weaknesses**
*   **Major:**
    *   Circular reasoning: Using a dataset as both the source of data and the object of trust analysis.
    *   Complete absence of comparative data or external validation to substantiate claims about credibility.
    *   Methodologically unsound and opaque "estimation" of demographic data.
    *   Severe confirmation bias driven by a predetermined theoretical and political stance.
    *   Conflates correlation with causation (e.g., high keyword frequency = intentional humanitarian framing for trust-building, rather than reflection of events).
*   **Minor:**
    *   Reduction of qualitative analysis to keyword counting.
    *   Overuse of jargon ("ethical infrastructure," "epistemic resistance") without operational precision.
    *   The date "July 2025" in the abstract and methodology is a clear error, as the paper is presumably written in the present.

**5. Recommendations for Improvement**
For this paper to be acceptable at a top-tier journal, the authors must fundamentally redesign the study.

1.  **Abandon the Circular Research Question:** The study must either:
    *   **A) Become a critical metadata analysis of ACLED itself:** Compare ACLED's sourcing, coding decisions, and event records against other datasets (UN, Israeli government, Palestinian health ministries, NGO reports) to empirically identify biases, gaps, and variations in "credibility construction." Or,
    *   **B) Study trust perception directly:** Conduct surveys or experiments with different audiences (policymakers, academics, public) to see how presentation of ACLED data versus other data influences perceived credibility.
2.  **Remove the Demographic Estimation:** Excise Table 5 and all conclusions drawn from it entirely. It is irredeemable without a fully specified and validated model.
3.  **Conduct a Genuine Qualitative Analysis:** If qualitative analysis is retained, it must move beyond keyword counts. Perform a rigorous, interpretive thematic analysis on a representative sample of narratives, acknowledging multiple possible interpretations and contextual factors.
4.  **Add Context and Baselines:** Provide pre-conflict baselines and comparisons to other conflicts to give the descriptive statistics meaningful context.
5.  **Tone Down the Rhetoric:** Replace advocacy-oriented language ("epistemic injustice," "moral witnessing" as a given) with neutral, scientific terminology that allows the data to speak for itself.

**6. Verdict**

**Overall Score: 1 - Strong Reject**

**Justification:** This paper has serious, fundamental flaws that preclude publication in its current form. The core methodological approach is circular and invalid for its stated research aims. The statistical estimation of demographics is unsound and unethical in its lack of transparency. The analysis is suffused with confirmation bias that treats a complex dataset as a simple proxy for ground truth. While the topic is of great significance, the study's design and execution do not meet the standards of rigorous science required for a high-impact journal. The path to acceptance would require a complete reconceptualization and redesign of the research, not merely revisions.

\newpage

\section{Human Reviewer}

Reviewer: Kaleem Ullah

Scores (out of 10):
- Question 1: 0.5
- Question 2: 0.5
- Question 3: 1
- Question 4: 0.25
- Question 5: 0.75
- Question 6: 0.5
- Question 7: 1

Total Score: 4.5 / 10

Reviewer Notes:


Submitted at: 2025-12-24 18:37:37


\newpage

\section{Aggregate Report}

Aggregate Review Report

This report combines the assessments from all three reviewers:

Individual Scores:
- AI Reviewer 1 (Comprehensive): 6.0 / 10
- AI Reviewer 2 (Critical): 0.0 / 10
- Human Reviewer (Kaleem Ullah): 4.5 / 10

Aggregate Score: 3.50 / 10

Overall Assessment:
The paper received an average score of 3.50 out of 10 across all three reviewers.

Final Decision: Major Revision

Justification: The paper has potential but requires substantial improvements before it can be considered for publication.

Generated: 2026-06-07 10:24:11


\end{document}
