Connecting Data Science and Qualitative Interview Insights through\n Sentiment Analysis to Assess Migrants' Emotion States Post-Settlement
Bibliographic record
Abstract
Large-scale survey research by social scientists offers general\nunderstandings of migrants' challenges and provides assessments of\npost-migration benchmarks like employment, obtention of educational\ncredentials, and home ownership. Minimal research, however, probes the realm of\nemotions or "feeling states" in migration and settlement processes, and it is\noften approached through closed-ended survey questions that superficially\nassess feeling states. The evaluation of emotions in migration and settlement\nhas been largely left to qualitative researchers using in-depth, interpretive\nmethods like semi-structured interviewing. This approach also has major\nlimitations, namely small sample sizes that capture limited geographic\ncontexts, heavy time burdens analyzing data, and limits to analytic consistency\ngiven the nuances of qualitative data coding. Information about migrant emotion\nstates, however, would be valuable to governments and NGOs to enable policy and\nprogram development tailored to migrant challenges and frustrations, and would\nthereby stimulate economic development through thriving migrant populations. In\nthis paper, we present an interdisciplinary pilot project that offers a way\nthrough the methodological impasse by subjecting exhaustive qualitative\ninterviews of migrants to sentiment analysis using the Python NLTK toolkit. We\npropose that data scientists can efficiently and accurately produce large-scale\nassessments of migrant feeling states through collaboration with social\nscientists.\n
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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 | 0.053 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".