MétaCan
Menu
Back to cohort
Record W4396235159 · doi:10.53555/sfs.v10i1.2643

Ethnographic Approaches In Disaster Management Among Indian States- A Comparative Study

2023· article· en· W4396235159 on OpenAlexvenueno aff
Resmi V. S, Smitha S, Prof. Asha J.V, Anil A.R

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousTraditional knowledgeEthnographyPreparednessNatural disasterNatural (archaeology)Emergency managementSociologyEnvironmental ethicsPublic relationsPolitical scienceGeographyEnvironmental planningEngineering ethicsEngineeringEcologyAnthropologyLaw

Abstract

fetched live from OpenAlex

Indigenous knowledge refers to the understandings, skills, and philosophies by societies with long histories of interaction with their natural surroundings. For rural and indigenous peoples, local knowledge informs decisions making about fundamental aspects of day-to-day life. The Indigenous traditional knowledge found in local communities in India is an amalgation of strategies, skills, rules and techniques gained through shared adaptive man- environment interactions to live and survive in the natural way of life. There is much to learn from indigenous and community-based approaches, for the natural disaster preparedness.  The people have developed their own strategies and traditional knowledge, and practices provide an important basis for facing even greater challenges of natural disasters.  Although their strategies may not succeed completely, they are effective to some extent and that is why the people continue follow those. The present paper tries to explore the Ethnographic approaches in Disaster Management practiced in select Indian States.

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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.384
GPT teacher head0.359
Teacher spread0.024 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Survey in Fisheries SciencesSame topicDisaster Management and ResilienceFrench-language works237,207