Opportunities, limits and challenges of perceptions studies for humanitarian contexts
Bibliographic record
Abstract
This article aims to advance understanding and discussion of perceptions studies as a method for strengthening humanitarian performance. Perceptions studies are qualitative studies produced for and often by humanitarian organisations, based on analysis of local perceptions of humanitarian efforts. While these studies are normatively asserted as valuable within the humanitarian sector, there has been no synthesis to date of their potential and limitations. This critical review of 59 perceptions-related documents responds to that gap, outlining key assertions of the value added and challenges of using perceptions studies in humanitarian work. While the objective is to inform and strengthen future use of this method, the perceptions literature also points to significant tension between this qualitative method and dominant expectations in humanitarian monitoring and evaluation.
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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 | 0.307 | 0.346 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.024 | 0.044 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.006 | 0.010 |
| 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".