Sri Lankan Expatriate Scientists in Vancouver: Attitudes towards returning
Bibliographic record
Abstract
development through their burgeoning high tech industries. For a country that has already been ravaged by 20 years of civil war, the tsunami only exacerbated their economic and political woes. Like many South Asian countries, Sri Lanka has a large expatriate community – many of whom are accomplished scientists working in Western countries like Canada. MOST recognized this issue prior to the tsunami and have been working on a recruiting campaign to entice Sri Lankan expatriate scientists back to help rebuild the nation. The tsunami added a note of urgency to this effort as expatriate scientists are needed now, more than ever, to aid with the reconstruction of Sri Lanka. Adam Holbrook agreed to assist MOST with their efforts to recruit expatriate scientists back to Sri Lanka by conducting field research in Vancouver on the Sri Lankan population to determine what might entice some of these expatriates to return. The task of managing the research was given to Aaron Cruikshank, a
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".