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Record W314518118

Indigenous Peoples and the Collaborative Stewardship of Nature

2012· article· en· W314518118 on OpenAlexaboutno aff
Denis Bryne

Bibliographic record

VenueArchaeology in Oceania · 2012
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousStewardship (theology)Government (linguistics)Equity (law)Environmental ethicsEconomic JusticePolitical scienceArchaeologyGeographyEthnologySociologyLawEcology
DOInot available

Abstract

fetched live from OpenAlex

Indigenous Peoples and the Collaborative Stewardship of Nature By Anne Ross, Kathleen Pickering Sherman, Jeffrey G. Snodgrass, Henry D. Delcore and Richard Sherman. Walnut Creek, CA: Left Coast Press, 2011. ISBN 978-1-59874-577-1 (hb)/-578-8 (pb). Pp. 320. $US89 (hb), $US29.95 (pb). This book presents a clear-eyed critical overview of efforts made to date to forge workable partnerships in the manag ement of natural resources between Indigenous groups on the one hand and conservation scientists and government agencies on the other. The news is for the most part not good, or perhaps one should say that there is good to be found but you have to look for it among the plethora of initiatives which, however well motivated, rarely deliver equity or real justice to Indigenous peoples. Many of these initiatives involve collaborations between conservation biologists (often under the auspices of one or another of the IUCN program areas) and Indigenous groups. Archaeologists in Australia, Canada, and the USA often become involved in this area via 'community archaeology' projects with Indigenous groups and via the latter's holistic conception of landscape. One of the great values of this volume is that it combines case studies of Indigenous stewardship of nature in former settler colonies (Aboriginal groups in southwest Queensland and the Lakota Native American people at Pine Ridge in South Dakota) with case studies of Indigenous minority groups in Thailand (tribal minority groups in Nan Province) and India (the Dhil tribal minority on southwest Rajasthan). The authors identify a series of epistemological and systemic (or institutional) obstacles to Indigenous involvement in natural resource management (NRM). The former are mainly to do with difficulties encountered in achieving recognition of Indigenous knowledge as a valid basis for landscape or NRM management and this points to a key sticking point: Western biological science is recognised by the modern state as the proper knowledge in this field and Western biological science exercises various forms of closure against other forms of knowledge. These include an insistence that Indigenous knowledge conform to the same methods and standards of proof that operate in science disciplines. On the Indigenous side, environmental knowledge tends to be integrated in complex ways with religious belief and often this entails a conviction that habitats and species embody spirits and spiritual forces which give them a degree of agency in relation to humans. The rationalisation of the Western mind which has been proceeding apace since the Protestant Reformation ensures that such beliefs are regarded as superstitious, absurd, and as a recipe for economic backwardness. But Western conservationists must contend with the reality that a large proportion of the biodiversity presently extant in the non-Western world has survived precisely because such belief systems have protected it--witness for instance the sacred forest groves of India, Africa, and parts of Southeast Asia. …

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)0.003
metaresearch head score (Gemma)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.012
Scholarly communication0.0070.006
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.012
GPT teacher head0.337
Teacher spread0.325 · 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".

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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