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Remembering Droughts and Abundance: Ecological Memory in the Semi-arid Region of Northeast Brazil

2024· article· en· W4404120676 on OpenAlexvenueno aff
Renan Martins Pereira

Bibliographic record

VenueAnthropologica · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsAbundance (ecology)AridGeographyEcologyBiology

Abstract

fetched live from OpenAlex

In Floresta, a municipality in the semi-arid region of Northeast Brazil, the past is considered a time of greater regularity of the climate, natural wealth of the Caatinga biome, and solidarity among the residents of the city’s rural area. Memories of certain historical events can refer to an ‘ancient time’ in which ‘abundance,’ despite the calamities generated by droughts throughout the occupation of this territory, is considered one of its main socio- ecological attributes. In the context of the violent climatic transformations of contemporaneity, analyzing the actions of the memory of old inhabitants of the Brazilian semi-arid region will be a way of first, ethnographically questioning the history of droughts to which the entire ecology of this region has been reduced by historiography and national literature; second, inquiring the human centrality of modern memory—still today a defining theoretical paradigm of social and cultural studies of memory in anthropology. To this end, memories of droughts and abundance will be articulated with the concept of “duration” (la durée) of French philosopher Henri Bergson to think of memory and the duration of time as a way of ecologically living the past, the present, and the future.

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 score0.023
Threshold uncertainty score0.729

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.249
Teacher spread0.232 · 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
Published2024
Admission routes1
Has abstractyes

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