Magic bullet or questionable remedy? Discussing the use of Meta advertising to recruit hard-to-reach migrants for surveys
Bibliographic record
Abstract
Abstract As the world sees an increase in global migration, it is paramount to collect real-time data on migrants to facilitate their settlement in new countries. A major obstacle to capturing such information is that many migrants, such as refugees, are “hard-to-reach.” Traditional sampling methods often miss small and mobile populations and for this reason new methods are needed. Survey recruitment through Meta advertisements is one such approach. By means of an auto-ethnographic discussion, we offer practical insights into different methodological considerations that researchers must contemplate when using advertisements on Meta’s Facebook and Instagram platforms to sample refugees as hard-to-reach populations. Our exchange focuses on recruitment and response rate issues, ethical considerations, the validity of data shared, and the opaqueness of how ad targeting algorithms work. This is followed by some concluding observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.312 | 0.425 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".