Natural Sciences Meet Social Sciences: Census Data Analytics for Detecting Home Language Shifts
Bibliographic record
Abstract
As we are living in a global environment, it is not unusual to have more than one languages or dialects used in a country. Examples include Canada in the Americas, Singapore in Asia, and Switzerland in Europe. With the initiatives of globalization, many people immigrate or live in a country other than their birthplace. As a result, different people in the same country may have different home language (i.e., first language). For instance, as a nation composed of a highly diverse language population, Canada provides a unique opportunity to study the factors causing certain languages (or families of language) to be lost over subsequent generations among allophones (i.e., people whose mother tongue is neither English or French). In this paper, we focus on census data analytics. Specifically, we analyze census microdata by exploring machine learning and data mining techniques-such as decision tree induction, random forest, and categorical naive Bayes-to study the influence of various social and economic factors on the probability that allophones adopt official languages as their language spoken at home. This study is a showcase where natural sciences and engineering (NSE) meet social sciences, in which NSE solutions (e.g., census data analytics) are applicable for the study of social science related phenomena (e.g., successful detection of shifts in home languages).
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".