Bibliographic record
Abstract
siglo 21. Sorprenden los prejuicios sobre su magnitud real (Arango, 2007): solo uno de cada 40 habitantes del Sur tiene la condition de emigrante y los 191 millones de migrantes mundiales actuales han crecido por debajo de lo que lo he cho la poblaci?n humana desde 1970... M?s relevante, en cambio, es su vistosidad: si a principios del siglo XX nueve de cada diez migrantes se dirigian a solo cinco estados (EUA, Argentina, Brasil, Canada y Australia) y habitualmente en direction norte-sur o con el proyecto de constituir ?nuevas Europas? (Crosby, 1988), ahora se les han anadido Europa occidental, el Golfo Persico y el Pacffico occidental; y entre las areas de origen, dominan Asia, America Latina y Africa. En esta creciente transformation multicultural hay que buscar la raiz de la psicosis securitaria que corroe Europa occidental. Quisieramos llamar aqui la atenci?n sobre las in terrelaciones entre la globalization liberal, el auge de la economia especulativa y la localization desigual de mano de obra barata emigrante alia donde el turismo y el boom constructor marcan el paso. Ayer, por ejemplo, en Baleares -^^^H y hoy tambien en el Sur. ^^^B
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".