REVIEWER 2 - CRITICAL REVIEW
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**Overall Impression**

My immediate reaction is one of significant methodological concern. While the topic of epistemic justice in humanitarian data is timely and important, this manuscript suffers from fundamental flaws in research design and execution that undermine its scientific validity. The study presents as a mixed-methods investigation but relies on "simulated interviews" - a methodological red flag that raises serious questions about data authenticity. The quantitative analysis, while technically sound, examines a dataset with questionable representativeness and appears disconnected from the stated research questions about trustworthiness. This feels like an overhyped weak study attempting to leverage a politically charged context to mask methodological deficiencies.

**Technical & Scientific Assessment**

**A. Problem Definition (Score: 3/5)**
The research question is clearly motivated by important theoretical frameworks (epistemic justice, communicative competence) and addresses a genuine problem in humanitarian practice. However, the connection between the quantitative analysis of damage data and the qualitative investigation of trustworthiness remains conceptually weak. The authors fail to convincingly argue why statistical correlations between damage variables should inform our understanding of institutional trust.

**B. Methodological Soundness (Score: 1/5)**
The use of "simulated interviews" represents a fatal methodological flaw. Without transparency about what "simulated" means (AI-generated? role-play? fabricated narratives?), the qualitative data cannot be considered valid evidence. The sampling strategy (n=18) lacks justification for sufficiency, and the absence of details about interview simulation procedures violates basic research transparency standards. The quantitative methods are technically adequate but applied to data that may suffer from selection bias and unknown verification protocols.

**C. Results & Evidence (Score: 2/5)**
The quantitative results demonstrate statistical coherence but don't actually address the core research questions about trustworthiness. High correlations between damage variables could simply reflect coordinated reporting rather than data quality. The qualitative "findings" are fundamentally compromised by the simulation methodology. Claims about "moral-trust statements" and their statistical relationship to correlation coefficients are speculative at best, given the artificial nature of the interview data.

**D. Contribution to the Field (Score: 2/5)**
While the integration of epistemic justice frameworks with humanitarian data science is potentially valuable, the methodological flaws prevent this study from making a meaningful contribution. The central finding - that community data shows internal consistency - is unsurprising and doesn't substantially advance our understanding of trust establishment.

**E. Writing & Presentation (Score: 3/5)**
The paper is generally well-written and logically organized, with appropriate theoretical framing. However, the persistent use of "simulated interviews" without adequate explanation creates confusion and undermines credibility.

**F. Ethical & Transparency Standards (Score: 1/5)**
The failure to disclose the nature of "simulated interviews" represents a serious transparency violation. There's no indication of whether IRB approval was obtained for human subjects research involving simulated participants. The dataset is cited but without critical examination of its collection methodology or potential biases.

**Strengths**

- Important theoretical framing combining epistemic justice with humanitarian data practices
- Clear articulation of the problem of institutional skepticism toward community data
- Technically competent quantitative analysis
- Appropriate use of mixed-methods terminology and design description

**Weaknesses**

**Major Flaws:**
- Unvalidated "simulated interview" methodology that compromises all qualitative findings
- Disconnect between quantitative results and research questions about trustworthiness
- Lack of critical examination of the Kaggle dataset's collection methodology and potential biases
- Insufficient justification for sample size and sampling strategy
- Overstated claims about the relationship between statistical correlations and trust establishment

**Minor Flaws:**
- Inconsistent citation formatting in references
- Vague description of "NVivo-style coding" without specific procedures
- Unclear how "moral-trust statements" were operationalized and quantified

**Recommendations for Improvement**

1. **Replace or substantially validate the qualitative component:** Conduct genuine interviews with actual participants or provide exhaustive documentation of simulation procedures with validation evidence.

2. **Strengthen theoretical connections:** Develop clearer conceptual links between statistical coherence and trustworthiness, perhaps through mediation analysis or additional validation metrics.

3. **Address dataset limitations:** Critically examine the Kaggle dataset's collection methodology, potential biases, and representativeness.

4. **Improve methodological transparency:** Provide detailed protocols for all data collection and analysis procedures, including explicit ethical oversight documentation.

5. **Converge methods more meaningfully:** Design integration procedures that actually test hypotheses about the relationship between data quality and trust establishment.

**Verdict**

**Overall Score: 1/5 - Strong Reject**

**Justification:** This paper cannot be accepted in its current form due to the fundamental methodological flaw of using "simulated interviews" without adequate explanation or validation. This compromises the entire qualitative component and, by extension, the mixed-methods integration. Even if this issue were resolved, significant concerns remain about the conceptual disconnect between the quantitative findings and the research questions about trustworthiness. The study attempts to address an important problem but fails to provide scientifically valid evidence for its conclusions. A complete redesign with authentic data collection would be necessary for reconsideration.