The Concept of "Conflict Sensitivity" and Its Application by Country and Field Office Staff During Implementation of Humanitarian Programmes: Case Study of Save the Children Canada
Bibliographic record
Abstract
Conflict Sensitivity, as an approach to deliver humanitarian assistance, emerged in the humanitarian field two decades ago. However, to this day, there is no commonly agreed upon definition of Conflict Sensitivity nor agreement of what Conflict Sensitivity implies. Using a practitioner-based approach, and applying concepts on norm consolidation, I explored how the concept of “Conflict Sensitivity” has been understood and approached by humanitarian teams delivering Save the Children Canada's humanitarian projects in fragile contexts. To ensure that I could capture how Conflict Sensitivity is approached by teams delivering humanitarian assistance in two exceedingly different settings, I selected Nigeria—where Save the Children has a longstanding experience of responding to conflict-affected populations—and Venezuela—where currently there is a relatively new response to the needs of the population in a fragile context. The findings reveal that the understanding and operationalization of Conflict Sensitivity is shaped by the context where teams deliver humanitarian assistance. Findings from the Nigeria office emphasize the importance of not exacerbating existing tensions in the society. In contrast, those working in Venezuela applied elements of Conflict Sensitivity in navigating complex political situations and shrinking humanitarian space. By focusing my research on the actions of those staff members who deliver humanitarian projects directly to populations in need, the research was able to provide insights into how Conflict Sensitivity is applied in day-to-day operations; thereby allowing us to draw conclusions about the different ways on-the-ground practices can influence organizational practice and policies.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.045 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".