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Record W6949870821 · doi:10.5281/zenodo.3731032

Cryptocurrencies and the future of money. Money and trust in Mexico

2020· article· en· W6949870821 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHyperinflationCurrencyInflation (cosmology)Latin AmericansCryptocurrencyLegal tenderCirculation (fluid dynamics)

Abstract

fetched live from OpenAlex

As official legal tender in Mexico, Canada and the United States until the mid-1800s, the Mexican Peso is one of the oldest currencies in North America. Today, Mexico’s currency is the 15th most traded in the world, and is the the most traded of all Latin American countries1 . This reflects the strength of both the Mexican economy and it’s currency. The current denomination of the Mexican Peso comes from 1993, when, after the high inflation of the late 80s, president Carlos Gortari stripped three zeros from the Peso creating the Nuevo Peso (New Peso). In 1996, the word Nuevo (new) was removed and the currency was once again named Peso (without changing its denomination). Although Mexico faced periods of high inflation during the mid-90s, with almost 35% of annual inflation in 1995 and 1996, this can’t be compared with the hyperinflation of other Latin American countries (Argentina and Brazil) during the 90s. Therefore, we can say that, overall, since the implementation of the New Peso in 1993, Mexico’s currency has been fairly stable, helping to boost the country’s economic development in the 21st century

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 score0.035
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0050.005
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.031
GPT teacher head0.255
Teacher spread0.224 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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