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Record W2575562953 · doi:10.1080/24694452.2016.1260438

Applied Montology Using Critical Biogeography in the Andes

2017· article· en· W2575562953 on OpenAlexaff
Fausto O. Sarmiento, José Tomás Ibarra, Antonia Barreau, J. Cristóbal Pizarro, Ricardo Rozzi, Juan Antonio González, Larry M. Frolich

Bibliographic record

VenueAnnals of the American Association of Geographers · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsUniversity of WaterlooUniversity of British Columbia
FundersComisión Nacional de Investigación Científica y Tecnológica
KeywordsBiogeographyAnthropoceneGeographyIndigenousBiodiversitySituatedEcologyLandformSociologyEnvironmental ethicsCartography

Abstract

fetched live from OpenAlex

More than most other landforms, mountains have been at the vanguard of geographical inquiry. Whether promontories, cultural works on slopes, or even metaphorical/spiritual heights, mountain research informs current narratives of global environmental change. We review how montology shifts geographic paradigms via the novel approach of critical biogeography in the Andes. We use it to bridge nature and society through indigenous heritage, local biodiversity conservation narratives, and vernacular nature–culture hybrids of biocultural landscapes (BCLs), focusing on how socioecological systems (SES) enlighten scientific query in the Andes. In our Andean study cases, integrated critical frameworks guide the understanding of BCLs as the product of long-term human–environment interactions. With situated exemplars from place naming, wild edible plants, medicinal plants, sacred trees, foodstuffs, ritualistic plants, and floral and faunal causation, we convey the need for cognition of mountains as BCLs in the Anthropocene. We conclude that applied montology allows for a multi-method approach with the four Cs of critical biogeography, a model that engages forward-looking geographers and interdisciplinary Andeanists in assessments for sustainable development of fragile BCLs in the Andes.

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)0.000
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 score0.019
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.026
GPT teacher head0.317
Teacher spread0.291 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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