Bibliographic record
Abstract
We report results from a randomized field experiment conducted in the Tigray region of Ethiopia, which tests the impact of interaction with migrants on host community members' attitudes towards migrants.In three treatment groups, host community members were randomly paired with a migrant from a nearby refugee camp to play an incentivized guessing game.In the first of these treatments, the game was neutral in content, in the second it introduced subtle cues to economic matters, and in the third subtle cues to ethnic identity.In a fourth treatment, host community members were paired with other host community members to play the neutral game, and in the control condition host community members did not interact with anyone.The results show that, compared to the control group, interaction with a migrant significantly improved attitudes towards them.Subtle cues to economic matters or identity did not diminish this effect.However, we see similar effects on attitudes to migrants in the treatment group where hosts interacted with other hosts, which suggests that the effects are driven by human interaction in general, rather than by interacting specifically with a migrant.The effects of interaction are not much affected by the characteristics of the paired hosts and migrants, though host respondents in low skill occupations appear to respond more favourably to the treatments.Interestingly, however, we find no effects of the treatments on how migrants believe they are perceived by host community members.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".