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Record W4412746577 · doi:10.1086/736280

Payment ecologies

2025· article· en· W4412746577 on OpenAlexaff
Daromir Rudnyckyj

Bibliographic record

VenueHau Journal of Ethnographic Theory · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBusinessPaymentFinance

Abstract

fetched live from OpenAlex

University of VictoriaIn her Lewis Henry Morgan Lectures, delivered in 1997 and published under the title Marginal gains in 2004, Jane Guyer describes what she calls a "jungle of currencies" circulating in the Congo River basin in the early twentieth century (Guyer 2004: 32).Guyer focuses on a collection made by a Belgian colonial official, Van de Weyer, who meticulously documented sixty-eight items, including tools, weapons, adornments, musical instruments, and various kinds of raw materials.Guyer notes that "items identified as money figure prominently" in the inventory and include objects such as brass wire, iron machetes, and slender metal currency objects variously called monsea, bokana, and kwa (2004: 35).In this vibrant zone of riverine trade, Guyer explains that the Libinza region was characterized by "an intricate multiplicity of currencies" that formed "a vast exchange corridor" along the Congo River and its tributaries.Guyer illustrates this monetary multiplicity, as follows:Even the most minor transaction was an asymmetrical conversion in the sense that it assumed directionality; it referred back to a place, a localized value and a past and was endowed with a new attribute in the transaction that afforded "candidacy" . . .for travel along new pathways.The multiple coexisting currencies symbolized, mediated, and were themselves items in the directional orientation of the flow of goods.In every case, either the final money forms or the materials to make them originated from outside Libinza society.All were, in fact, interconvertible through denominational processes, but at one time, each currency within each group was more or less oriented toward one kind of transaction versus another.In principle, at one time, only one currency in each society, mediated social payments-fines, bridewealth, and so on-and others mediated the purchase of utilities, such as foodstuffs, and productive investments, such as canoes.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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