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Record W2971536754

Navigating science in society: Multiple Ways of Knowing - Exploring the Commonalities and Differences between Indigenous Knowledge and Science

2018· other· en· W2971536754 on OpenAlexaboutno aff
Daniel Hikuroa

Bibliographic record

VenueResearchSpace (University of Auckland) · 2018
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicIndigenous Knowledge Systems and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsTraditional knowledgeIndigenousSociology of scientific knowledgeEpistemologySociologyEngineering ethicsSocial scienceEngineeringBiologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

6th International Conference on Science in Society, Vancouver, Canada. Indigenous peoples worldwide have had varied interactions with science, and similarly the science academy’s relationship with the ‘indigenous’ has also varied. For many years indigenous knowledge (IK) has been considered incompatible with western empirical based science, mainly due to differences in knowledge inquiry and transfer, as well as more fundamental beliefs about the inseparable nature of material and non-material aspects of the universe held by the former. Increasingly however, commonalities between the two are being recognised. Both scientists and IK holders, and in particular practitioners, are beginning to work with each other. The recognition that aspects of IK have been generated following the scientific method affords the exciting opportunity to explore how IK can be integrated with science to add to our collective understanding. In this paper I will (i) demonstrate that in the field of natural hazards and disasters research IK can be viewed as an encoded database of natural hazard events and (ii) discuss a method by which to un-encode the database. Accordingly, this research affords the unique opportunity to explore and build a relationship between IK and science, in essence putting indigenous society into science.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.077
GPT teacher head0.258
Teacher spread0.181 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2018
Admission routes1
Has abstractyes

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