Bibliographic record
Abstract
Language nests are one of the most crucial methods of language revitalization. The conservation and reclamation of endangered and/or oppressed languages is a critical scientific and ethical endeavor. Intergenerational transmission is the most significant factor in determining the endangerment (or conversely, the vitality) of a language, and language nests are the method of revitalization that most directly addresses the challenge of creating a new generation of first language speakers, or language users in the case of sign languages. Language nests bring together the methods and theories of both language revitalization and child language acquisition. In 1998 the United Nations passed the Universal Declaration of Linguistic Rights, stating that people have a right to use their language both in public and in private and to preserve their culture and language. The first recorded early childhood language immersion programs of non-endangered languages were French immersion classes for young children in Quebec, Canada, in the 1960s. Sāmoan language nests in New Zealand were later developed for Sāmoan heritage speaking children in New Zealand. This method was then adopted by Māori speakers to revitalize the Māori language. Unlike Sāmoan heritage speakers in New Zealand, the Māori were not diaspora in the traditional sense of having left their homeland. However, as a result of language encroachment, many Indigenous people have become linguistic diaspora within their own homeland, in the sense that their language has become displaced from many of its prior domains of use. Indigenous people who do not speak their ancestral language and whose languages are severely endangered have explained that a challenge they face is that there are no locations learners of an endangered language can travel to where they can be completely immersed in the target language in a place where everyone speaks it fluently, where it is used in every domain. This is unique from other language situations, and unlike learners of more widely spoken languages like Spanish, Russian, Mandarin, or Arabic, who can participate in an immersive study abroad experience after taking classes. The language nest seeks to create a reservoir of an immersive experience, which has historically fed into the development of immersion schools, universities, and even graduate programs. Those who graduate from the immersive education, especially higher education, often deliberately and consciously introduce or re-introduce the language to ever increasing domains. The accelerated rate of language loss in the current and recent century requires these new approaches to language maintenance and language reclamation.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.343 | 0.228 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".