Inmigración y pluralidad lingüística: un reto para los derechos humanos
Bibliographic record
Abstract
Migration has become a reality of global dimension, a visible sign of a globalization process that is increasingly accused. Spain has not been immune to this phenomenon; becoming a multicultural reality which can bring people of different nationalities, ethnicities and ideologies together. This cultural and ethnic diversity is a challenge that faces not only Spain, but it is a trend that is becoming increasingly important across Europe. In this paper, especially addresses the linguistic identity as a key element for configuring an effective framework of human rights. Of particular significance is the analysis of model followed in a country -Canada- because is a paradigm of multiculturalism. Indeed, Canada was a pioneer in the world to proclaim officially back in 1971, its formal and firm commitment to multiculturalism and political governance of cultural diversity. In present worth also analysis the legal construction that has devised this country around "reasonable accommodation of rights" a novel concept that has attracted the attention of Europe, as plural society that can import this formula. And not only for Europe, since the situation of the cultural and linguistic environment of Quebec has clear parallels with the situations that exist in some regions of Spain, being an excellent model for complex societies such as The Basque Country and Catalonia, both linguistically plural societies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".