The Ethics of Naming in Forced Displacement Research: Critical Work and Policy Labels
Bibliographic record
Abstract
With a pedagogical aim, we offer an overview of some, though certainly not all, of the potential initial framing considerations in forced displacement research. We then engage with several of the key terms currently in use by international agencies before discussing how those terms can be (re)interpreted as they are taken up in transnational contexts. In attending to the ethics of naming throughout, we suggest that terms developed by international policy bodies should be approached situationally in disasters as part of humanitarian aid. Just as document-specific definitions need not go beyond the document, situation-specific terms should not become oppressive labels that have the potential to stigmatize people for the rest of their lives. Thus, we caution against assigning such terms as fixed identity categories, as they have the potential to reduce a person to a situation in which they may have once found themselves.
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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 | 0.309 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.030 | 0.178 |
| Scholarly communication | 0.035 | 0.039 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".