Measuring Resilience in the Context of Conflict-Related Sexual Violence: A Novel Application of the Adult Resilience Measure (ARM)
Bibliographic record
Abstract
There is a rich body of research addressing the issues of conflict-related sexual violence, and a similar wealth of scholarship focused on resilience. To date, however, these literatures have rarely engaged with each other. This article developed from an ongoing research project that seeks to address this gap, by exploring how victims-/survivors of conflict-related sexual violence in three highly diverse settings - Bosnia-Herzegovina, Colombia and Uganda - demonstrate resilience. This research is the first to apply the Adult Resilience Measure (ARM), a 28-item scale that seeks to measure protective resources across individual, relational, and contextual subscales, to the context of conflict-related sexual violence. A total of 449 female and male participants in the three aforementioned countries completed the ARM (in the framework of the study questionnaire) as part of this research. This article presents some of the results of the analyses. Specifically, we first sought to establish through Confirmatory Factor Analysis whether the ARM was actually measuring the same construct in all three countries, by confirming the invariance (or otherwise) of the factor structure. The second aim was to explore how different resources function and cluster in different cultural contexts, to arrive at a more nuanced understanding of the different protective factors in the lives of study participants. We generated different factor structures for BiH, Colombia, and Uganda respectively, suggesting that a single factor structure does not sufficiently capture the diverse groupings of protective factors linked to the particularities of each country, including the dynamics of the conflicts themselves. Ultimately, we use the findings to underscore the need for policy approaches that move away from a deficit model and give greater attention to strengthening and investing in the (often overlooked) protective resources that victims-/survivors may already have in their everyday lives.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".