Parent and child attitudes towards educational and occupational achievement as a function of acculturation
Bibliographic record
Abstract
As individuals and families move from India to Canada, they bring with them \nthe values, attitudes, and beliefs they held in their native country. During the process \nof building new lives in a new country the immigrants are immersed in a culture \ndifferent then their own. As the East Indian immigrant population has grown, so has \nthe number of first-generation, Canadian born children of East Indian parents. The \nfocus of this study was to identify the relationship between acculturation and \nattitudes held for both male and female children. In other words, this study looked at \nthe relationship between cultural factors (as determined by the level of parental and \nchild acculturation) within the family and attitudes toward occupational and \neducational achievement for male and female children. \nResults of this study found that East Indian parents placed significantly high \nimportance on both academic success and occupational success for their sons and \ndaughters. These expectations were understood and accepted by their children. \nFinally, the importance with which parents viewed educational and occupational \nachievements and their expectations for their children were not related to their level \nof acculturation, their Canadian or Asian cultural values, or their level of endorsed \nsex-role egalitarianism.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".