Methodological Challenges of Studying Asian Gangs in Canada
Bibliographic record
Abstract
This paper discusses the methodological challenges encountered in a research study on Asian gangs in Canada with reference to (a) defining Asian gangs; (b) seeking external funding; (c) securing support from ethnic communities; (d) getting approval for ethical research with human subjects; (e) recruiting research participants; (f) ensuring safety of the researcher; and (g) protecting the identity of research participants. The issues discussed would be relevant to those who are interested to examine the same phenomenon in countries with large Asian populations in Western counties (e.g., the United States and the United Kingdom) and in the Asia-Pacific Region such as Australia, Singapore, and Malaysia.
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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 | 0.051 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.036 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".