contains supplementary material, which is available to authorized users.
Bibliographic record
Abstract
The Author(s) 2014. This article is published with open access at Springerlink.com Abstract There is limited empirical evidence of how environmental conditions in the Global South may influence long-distance international migration to the Global North. This research note reports findings from seven focus groups held in Ottawa-Gatineau, Canada, with recent migrants from the Horn of Africa and francophone sub-Saharan Africa, where the role of environment in migration decision-making was discussed. Participants stated that those most affected by environmental chal-lenges in their home countries lack the financial wherewithal to migrate to Canada. Participants also suggested that internal rural–urban migration patterns generated by environmental challenges in their home countries underlay socioeconomic factors that contributed to their own migration. In other words, environment is a second- or third-order contributor in a complex chain of interactions in the migrant source country that may lead to long-distance international migration by skilled and edu-cated urbanites. These findings have informed the scope and detail of a larger, ongoing empirical study of environmental influences on immigration to Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.934 | 0.770 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".