Immigrants in Spain: sociolinguistic issues
Bibliographic record
Abstract
A general vision of immigration in Spain will be presented in the following pages, with an emphasis on the sociolinguistic aspects of the teaching/learning of Spanish as an L2. The changes that Spain has undergone over the last quarter century have been so intense that they have had an impact on the traditional migratory tendency of the country, causing it to pass from the producer of migrants to the receiver of immigrants. Figures support this statement: the fact, for instance, that a million and a half Spanish immigrants were still living abroad in 1995, while the number of foreigners residing in Spain at present has reached more than three million. In spite of the fact that Spain is still far behind neighboring countries in terms of the numbers of immigrants living there, immigration has contributed to the demographic rise and economic development of the nation. At the same time, however, it has also caused a series of social demands that have not always been met adequately; for example, the schooling of child and teenage immigrants, access to the labor market for immigrant workers, access to the health system, housing, etc. Also, for a high percentage of the immigrant population, the learning of the Spanish language becomes one of their primary necessities upon arrival. This need, in the case of children and teenagers, is being met by the educational institutions, though with differing results. A lack of an official Spanish as an L2 curriculum that combines general communicative as well as academic competence causes the Spanish-language teaching programs to become less efficient.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".