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Record W4382813356 · doi:10.1093/jaarel/lfad030

Performing Symmetry in Andean Weather Forecasting: The Practice of Cabañuelas, Beyond Divination and Suffering

2022· article· en· W4382813356 on OpenAlexafffund
Jorge Legoas P.

Bibliographic record

VenueJournal of the American Academy of Religion · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsQueen's University
FundersQueen's University
KeywordsMAGIC (telescope)Performative utteranceGestureEpistemologyDivinationReading (process)AsideHistoryLotteryLinguisticsSociologyPhilosophyLiteratureArtMathematics

Abstract

fetched live from OpenAlex

Abstract This paper analyzes a practice known as Cabañuelas—an Indigenous way of anticipating the next year’s rainfall by interpreting a series of signs in the current year. The knowledge and gestures associated with this practice have traditionally been understood according to the classical division between pragmatic techniques and magic rituals, an approach that I set aside in favor of considering Cabañuelas as a hybrid practice involving multiple kinds of beings and agents. It can also be understood as part of a complex of practice together with the offerings to the earth in August, and in addition to the reading of the coca leaf and the molten lead—all of these being practices that share an observational, prospecting, and performative character. Based on this, I put forward the idea of empowered agents, challenging the view of suffering Andean actors hopelessly subjected to super/natural forces or idiosyncratic conditions they cannot control.

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)0.004
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.024
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.331
Teacher spread0.310 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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