Spanish, second language of the internet? The hispanic web, subaltern-hybrid cultures, and the neo-liberal lettered city
Bibliographic record
Abstract
Se aborda en este art?culo la ubicaci?n de la lengua espa?ola en internet. Tras denunciar el discurso pol?tico-intelectual oficial como desconectado anacr?nica mente de la realidad, se proporcionan aqu? datos sobre la utilizaci?n del espa?ol en internet durante los a?os 2001-2005. Despu?s de considerar varios factores (usuarios, acceso, creaci?n de p?ginas web, GDP), el art?culo llega a la conclusi?n de que el espa?ol sigue siendo un lenguaje minoritario en internet, desmintiendo as? las pretensiones oficiales de ser una segunda lengua,pretensi?n que equivale a una nueva forma global de ideolog?a de la hispanidad. Bas?ndonos en las m?s importantes teor?as geopol?ticas sobre la cultura desarrolladas en el mundo hisp? nico (l?mite/periferia, sub alter nidad/hibridez, imperialismo/nacionalismo), llega mos a la conclusi?n de que sus oposiciones complementarias tienen que ser combinadas a fin de describir plenamente el estatus del espa?ol en internet. Este art?culo advierte que un fracaso en la combinaci?n de tales teor?as legitimar?a una nueva ciudad letrada neoliberal y sus letrados putativos (Rama).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".