Bibliographic record
Abstract
Acculturation Identity capital Immigrants Individualization Schwartz, Montgomery and Briones begin the process of developing a model with which to understand the acculturation of immigrants within the context of iden-tity theory. To further the development of this model, in this commentary I will lay stress on the point that acculturation among immigrant people can be seen as a spe-cial form of the individualization of the life course. The requirement to individualize one’s life course, and hence one’s identity(ies), is endemic to late-modern societies, where the collectivist supports for developmental tasks found in traditional societies have diminished, leaving people to make more and more decisions regarding ‘who they are’ and what paths their lives should take [Beck, 1992; Cote, 2000]. Accord-ingly, the individualization of the life course can be a diffi cult task, even for non-im-migrant citizens with only one cultural/ethnic background. However, these diffi cul-ties can multiply as people’s circumstances become more complex, as in the cases discussed by Schwartz, Montgomery and Briones of non-white, non-Western immi-grants moving to Western nations. The situation becomes even more complex in countries that are ‘multicultural,’ as in Canada. In cities like Toronto, for example, there are now dozens of non-white ethnic groups with distinct languages living in close quarters, making it diffi cult for some minority members to even identify a ma-jority culture with which to acculturate. Moreover, experiences of discrimination are common
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".