Revisiting acculturation research with big data: the case of the Italian diaspora through the lens of Facebook interests
Bibliographic record
Abstract
Abstract This article leverages big data to contribute to acculturation research, tapping on population behavior to measure the proximity of an ethnic minority to majority and homeland orientations. Our data consists of anonymized information from the Facebook Advertising Platform Interface about active users who speak Italian on the platform and reside in the 16 countries with the largest Italian-speaking communities worldwide. We conduct two main analyses. First, by calibrating the volume of Italian-speaking Facebook users with the penetration rate of the platform by country, age and gender, we estimate that the Italian diaspora amounts to 5.66–5.95 million people globally (aged 18 or more). Second, we record the level of interest of Italian speakers in given topics covered by Facebook (called ‘Facebook interests’) and measure its (dis)similarity with the corresponding level among users in the country of residence and users in Italy as an indicator of Berry’s types of acculturation (integration, assimilation, separation, or marginalization). From our data, no overarching acculturation model prevails across the board. However, variability in the diaspora is lower when it comes to typical manifestations of ethnic heritage, for which the interests of Italian speakers are higher than among locals but lower than among homeland Italians. On the basis of such dissimilarities in interests, the Italian diaspora is segmented into three clusters, reflecting geographic and cultural areas: Italians in Latin America, the Anglosphere, and continental Europe.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".