Conservation²: The relationship between Kala language conservation and marine conservation in coastal Papua New Guinea
Bibliographic record
Abstract
In this paper we describe a collaboration among anthropologists, marine biologists and the Kala Language Committee in support of the combined goals of language revitalization, marine conservation and development of Kala elementary school curriculum. The Kala language, with four distinct dialects, is spoken in six coastal villages of Morobe Province, Papua New Guinea, where marine resources provide residents with the overwhelming majority of their dietary protein. In 2006, due to concerns about language shift, Kala speakers developed the Kala Language Committee (KLC). The KLCâs goals were to promote Kala rather than acquiesce to Melanesian pidgin (Tok Pisin), the national lingua franca, and English, the national language of education. A related goal was the documentation of Kala environmental knowledge because Tok Pisin, unlike Kala, uses general categories of description rather than words that convey specific information (e.g., species, sex, and age of animals). \n \n Concurrently with the language project, marine conservation research began in the Kala-speaking village of Kamiali. Primary goals were to document marine biodiversity, suggest ways to sustainably manage marine resources, and develop tools to help village residents evaluate the sustainability of their fishing practices. An early hurdle to information transfer to village residents was that no written language efficiently conveyed which species were under discussion; scientific names were overwhelming, whereas common English or Tok Pisin names were ambiguous. The problem was resolved in 2010 when the KLC developed and adopted a Kala orthography. The writing system permitted documentation of Kala fish names, which were subsequently used in publications and educational materials which serve to increase local comprehension of marine-conservation research and guidelines. Continued marine conservation research resulted in the development of a poster with sustainable fishing suggestions and a school curriculum for community-based marine-resource monitoring, which were fully translated into Kala. \n \n We argue that the attention paid to the Kala language via the KLCâs efforts has led to increased awareness of marine diversity and the importance of conservation, as well as to increased awareness of the etymology of Kala terms and associated cultural knowledge. Therefore, building on recent literature about the benefits of collaborative approaches to both language documentation and in situ biodiversity conservation, we argue here for the benefits of engaging with community members in projects that simultaneously document linguistic and ecological knowledge. As well, we argue that integrating the academic disciplines of linguistic anthropology, ecological anthropology and conservation biology can lead to better biolinguistic diversity conservation practices.\n \n References: \n \n Ban, Natalie, Chris Picard & Amanda Vincent. 2009. Comparing and integrating commu¬nity-based and science-based approaches to prioritizing marine areas for protection. Conservation Biology 23. 899â910\n \n Cinner, Joshua E., Michael J. Marnane, & Tim R. McClanahan. 2005. Conservation and Community Benefits from Traditional Coral Reef Management at Ahus Island, Papua New Guinea. Conservation Biology. 19. 1714-1723. \n \n Czaykowska-Higgins, Ewa. 2009. Research models, community engagement, and linguistic fieldwork: Reflections on working within Canadian indigenous communities. Language Documentation & Conservation 3(1). 15-50. \n \n Drew, Joshua & Adam Henne. 2006. Conservation biology and traditional ecological knowledge: Integrating academic disciplines for better conservation practice. Ecology and Society 11. 34â42.\n \n Grenoble, Lenore A. 2010. Language documentation and field linguistics: The state of the field. In Lenore A. Grenoble and N. Louanna Furbee (eds.) Language documentation:Practice and values, 289-309. Amsterdam: John Benjamins\n \n Guérin, Valérie and Sébastian Lacrampe. 2010. Trust me, I am a linguist! Building partnerships in the field. Language Documentation & Conservation 4. 22-33. \n \n Maffi, Luisa (ed.). 2001. On Biocultural Diversity: Linking language, knowledge and the environment. Washington: Smithsonian Institution Press.\n \n Ramstad, Kristina, N.J Nelson, G. Paine, D. Beech, A. Paul, P. Paul, F.W. Allendorf & C.H. Daugherty. 2007. Species and cultural conservation in New Zealand: Maori traditional ecological knowledge of Tuatara. Conservation Biology 21. 455â464.\n \n Si, Aung. 2011. Biology in Language Documentation. Language Documentation and Conservation 5. 169-186.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".