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Clouds, airplanes, trucks and people: carrying radioisotopes to and across Mexico

2015· article· en· W1939229375 on OpenAlexaboutno aff
Gisela Mateos, Edna Suárez‐Díaz

Bibliographic record

VenueDynamis · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Atomic energyTruckPolitical scienceNuclear weaponEngineeringGeographyEnvironmental protectionSociologyArchaeologyLawSocial science

Abstract

fetched live from OpenAlex

The aim of this paper is to describe the early stages of Mexican nuclearization that took place in contact with radioisotopes. This history requires a multilayered narrative with an emphasis in North-South asymmetric relations, and in the value of education and training in the creation of international asymmetrical networks. Radioisotopes were involved in exchanges with the United States since the late 1940s, but also with Canada. We also describe the context of implementation of Eisenhower's Atoms for Peace initiative in Mexico that opened the door to training programs at both the Comisión Nacional de Energía Nuclear and the Universidad Nacional Autónoma de México. Radioisotopes became the best example of the peaceful applications of atomic energy, and as such they fitted the Mexican nuclearization process that was and still is defined by its commitment to pacifism. In 1955 Mexico became one of the 16 members of the atomic fallout network established by the United Nations. As part of this network, the first generation of Mexican (women) radio-chemists was trained. By the end of the 1960s, radioisotopes and biological markers were being produced in a research reactor, prepared and distributed by the CNEN within Mexico. We end up this paper with a brief reflection on North-South nuclear exchanges and the particularities of the Mexican case.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
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 head0.101
GPT teacher head0.426
Teacher spread0.325 · 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 designNot applicable
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

Citations11
Published2015
Admission routes1
Has abstractyes

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