Replication Data for: Promises and Limits of Using Targeted Social Media Advertising to Sample Global Migrant Populations: Nigerians at Home and Abroad
Bibliographic record
Abstract
Survey research on migrants is notoriously challenging, especially if the goal is to collect data across a range of countries. Social-networking sites’ ability to micro-target advertisements to migrant communities combined with their near-global reach makes them an attractive option. Yet there is little rigorous evaluation of the quality of data thus collected – especially for populations from developing countries. We compare samples of Nigerian emigrants in Canada and Italy and Nigerians (at home) in Nigeria recruited through targeted advertising on Facebook and Instagram to population estimates. We find our samples contain varying degrees of bias in the case age and gender, and systematically miss those with little formal education. How much this affects our samples’ representativeness varies across contexts: discrepancies are much smaller for emigrant populations in Canada than in Italy and much larger in Nigeria, where a large share of the population has little formal education and limited literacy. Post-stratifying each sample on age, gender, and education does not ameliorate bias on other variables such as ethnicity, religion, period of migration, or political attitudes. We discuss the potential and limitations of social-media driven sampling and highlight key considerations for implementing it to collect multi-sited data on migrants. For Peer Review
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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 | 0.129 | 0.419 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.035 |
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".