MétaCan
Menu
Back to cohort
Record W4393280385 · doi:10.14244/rau.v14i2.426

Terras que renascem

2024· article· pt· W4393280385 on OpenAlexaff
Lucas da Costa Maciel, Fernanda Borges Henrique

Bibliographic record

VenueRevista de Antropologia da UFSCar · 2024
Typearticle
Languagept
FieldSocial Sciences
TopicUrban Development and Societal Issues
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

É possível pensar e agir numa era de catástrofes anunciadas? Mais ainda, é possível contar histórias em que, para além das catástrofes que continuam acumulando e que como tais já não são um anúncio, se viva a reconstituição e o renascimento? Se assim for, quais são as escalas relacionais a partir das quais isso se torna possível? Neste ensaio, partimos da criatividade reconstitutiva de dois coletivos, autoidentificados como Kiriri e Mapuche, para pensar o que mais pode ser dito quando todos os problemas parecem se reportar ao Antropoceno. Nossa intenção é ensaiar com outras escalas relacionais e, portanto, com outros problemas, a despeito da nova era geológica ou, se queremos, na vizinhança dela. Este texto resiste à conexão acelerada entre problemas de distintas ordens, com distintos nomes, e o Antropoceno. Interessa habitar os desajustes dessas conexões para colocar luz sobre a microfísica das relações, das entidades que elas convocam e que, a partir dos casos Kiriri e Mapuche, nos fazem ver terras que renascem. Interessa enfatizar mundos que se reconstituem e que multiplicam agentes de mudança através da replicação em que a complexidade da escala é a autossemelhança.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.005
Scholarly communication0.0060.004
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0290.008

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 head0.043
GPT teacher head0.343
Teacher spread0.301 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueRevista de Antropologia da UFSCarSame topicUrban Development and Societal IssuesFrench-language works237,207