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Record W2262772225

Conservation²: The relationship between Kala language conservation and marine conservation in coastal Papua New Guinea

2015· article· en· W2262772225 on OpenAlexaboutno aff
Ken Longenecker, Christine Schreyer, John A. Wagner

Bibliographic record

VenueAmericanae (AECID Library) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFishingDocumentationLingua francaGeographyMarine conservationSustainabilityEcologyLinguisticsComputer scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

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 KLC’s 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). 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. We argue that the attention paid to the Kala language via the KLC’s 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. References: 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. 899–910 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. 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. Drew, Joshua & Adam Henne. 2006. Conservation biology and traditional ecological knowledge: Integrating academic disciplines for better conservation practice. Ecology and Society 11. 34–42. 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 Guérin, Valérie and Sébastian Lacrampe. 2010. Trust me, I am a linguist! Building partnerships in the field. Language Documentation & Conservation 4. 22-33. Maffi, Luisa (ed.). 2001. On Biocultural Diversity: Linking language, knowledge and the environment. Washington: Smithsonian Institution Press. 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. 455–464. Si, Aung. 2011. Biology in Language Documentation. Language Documentation and Conservation 5. 169-186.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.034
GPT teacher head0.234
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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