Bibliographic record
Abstract
n Canada, as in other industrialized coun-tries, a high percentage of foreign-born res-idents are from the developing world (1). Some of these migrants are highly skilled scien-tists and engineers who constitute a “brain drain ” from their countries of origin (COs), but also represent a scientific diaspora with enor-mous potential. Scientific diasporas have been defined as “self-organized communities of expatriate scientists and engineers working to develop their home country or region, mainly in science, technology, and education ” (2). Unfortunately, many of these diaspora networks depend on a few champions for sustainability, and there has been evidence of Web site inactiv-ity (3) and ineffectiveness (4, 5). We believe scientific diasporas may repre-sent part of the solution to the often crippling economic and social effects of emigration on the developing world (5). However, systematic, qualitative research into the needs and percep-tions of the diasporas themselves regarding assisting their COs is lacking. Such informa-tion is essential to success of any future poli-cies aimed at engaging them. Using previously described qualitative research methods (6–8), we studied life sci-ence researchers and entrepreneurs during 2005 in three Canadian cities (Vancouver, Toronto, and Montreal) that represent strong
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.201 | 0.052 |
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".