The power of technology: a qualitative analysis of how South Asian youth use technology to maintain cross-gender relationships
Bibliographic record
Abstract
This research explores how South Asian youth in Canada use computer-mediated communication (CMC) such as social networking sites, cell phones and instant messaging in their cross-gender intimate relationships. Using 42 qualitative interviews conducted with second-generation South Asian Canadians living in the Greater Toronto Area and Durham region, this article sheds light on the motives for using CMC as well as negative consequences that can emerge. The data reveal that South Asian youth are using CMC to initiate and build relationships, remain connected with partners, engage in discreet communication, to ease uncomfortable and intimate discussions, and to communicate when face-to-face interaction is not available. Gender, religion and country of origin differences were rare, but did appear in a few motives. Negative consequences of CMC use volunteered by participants include parental–child conflict over restriction and questioning CMC use and its use leading to parents’ discovery of a ‘secret’ relationship. Overall, CMC provided a means for second-generation South Asian youth in Canada to overtly adhere to norms of gender-separation while covertly engaging in cross-gender relationships. If not discovered, this helped to maintain family honour within the South Asian community while fulfilling their perceived need for cross-gender friendships and romantic involvements.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".