Panorama actual y tendencias recientes de refugiados y solicitantes de refugio en el siglo XXI
Bibliographic record
Abstract
From 2000 to 2017, refugees and asylum flows have fluctuated greatly, generating ripple effects in countries, regions, and entire continents. These effects are felt deeply in countries of origin, as well as countries of destination and transit. From 2000 to 2017, the period that this article will be examining, Canada and the United States were the largest recipients of asylum requests in North America. Mexico received a growing number of requests, although to a lesser extent than its neighbors to the North. The following analysis situates refugee and asylum rates on a regional and global scale and invites us to examine and compare how the official discourse conforms to the actual policies that have been adopted in Central and North America, as well as examines interventions and frameworks for action that could promote both solidarity and public order. Realidad: Revista de Ciencias Sociales y Humanidades No. 156, 2020: 139-163
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".