Hizkuntza erdigunera: Urola Kostako adinekoen osasun arretan hizkuntzak duen eragina aztergai
Bibliographic record
Abstract
Language has a role to play in the complex world of healthcare, to the extent that we could say that it is the basis for the principal activity of healthcare. Canada is a pioneer in research that analyses the quality of healthcare and the language associated with it. The situation of bilingualism that we have in Euskal Herria means that we suffer from certain linguistic limitations, and older people are one of the most vulnerable groups in this respect. Here and now, being older and Basque means being subjugated. The objective of the project has been to analyse healthcare and the linguistic interaction of people of 65 years of age or more in the district of Urola Kosta. 89% of the respondents say that they would like to receive healthcare in euskera, and they attach great importance to the language (4.64/5) in the quality of the healthcare they receive. To address this, resources are needed as well as ears willing to listen. • Key words: older people, healthcare, quality, euskera
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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".