The Language Loss of the Indigenous
Bibliographic record
Abstract
List of tables & figures. List of Abbreviations. Notes on editor & contributors. Introduction: Aphasia: The Fate of the Indigenous Languages 1. Symbolic Power, Nation State and Indigenous Language: A Sociological Analysis of Tribes in Central India 2. Text, Subtext and Context of Indian Culture in the Developmental Paradigm of Globalization 3. Adivasi Art: The Convergence of the Intangible and the Tangible 4. Aesthetics of Representation: Media and Canadian Aboriginal Resistance 5. The Forgotten Tribe - The Kuravars Of Tamil Nadu 6. Tesu and Jhenjhi: A festival celebrating cultural life 7. Articulating Tribal Culture: The Oral Tradition of Lambadas 8. Micro and macro intergenerational oral communication in the Zion Christian Church 9. Sacred Places as Traditional Heritage for the Vhavenda Indigenous Community of Limpopo Province, South Africa 10. Translating Power, Gender and Caste: Negotiating Identity, Memory and History: A Study of Bama's Sangati 11. Historiography or Imagination? The Documentation of Traditional Luo Cultural Memory in Kenyan Fiction 12. Tackling Endangered Languages in the Midst of Diversity Lachman 13. Language Shift among the Waddar Speakers 14. Towards a Revitalization of Urhobo: An Endangered Language of Delta State, Nigeria 15. Itsekiri: Threatened and Endangered 16. Written Kikuyu as an Under-Developed Language Form: Evidence from an Exploration of its Phonology and Orthography 17. Endangered! The Igbo Language Dilemma in Nigeria 18. Aspects of Discourse Structure: A Case of Particles 19. Mobile Telephone Communication and the Akan Language Glossary. Index.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.106 | 0.018 |
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".