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Record W3137219633 · doi:10.24908/ijesjp.v8i1.14279

Science, technology and Solidarity

2021· article· en· W3137219633 on OpenAlexvenueno aff
Juan David Reina-Rozo, Luis Fernando Medina-Cardona

Bibliographic record

VenueInternational Journal of Engineering Social Justice and Peace · 2021
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsSolidarityOpen scienceXenophobiaPolitical scienceContext (archaeology)CommonsPublic relationsSociologySociology of scientific knowledgeEngineering ethicsPoliticsEnvironmental ethicsSocial scienceEngineeringLaw

Abstract

fetched live from OpenAlex

Science and technology are changing. We have seen the emergence of open and citizen-based science practices in the context of facing pandemics, such as COVID-19, xenophobia, or inequality, among others. Open science is a movement that advocates the collective construction of knowledge. This perspective has shown its importance with the emergence of rapid response initiatives to the current situation at national and international levels. This article discusses the relevance of knowledge commons and transparent objects in the era of intellectual property. Solidarity technoscientific initiatives become a vehicle to pose free culture as a pillar of a human future based on mutual support. In that sense, universities, publishers, students, the scientific and engineering community, and even citizens are creating efforts around open science intending to share results, data, designs, specifications, and even resources despite new socio-political limits and precautions. We argue that a technoscientific movement based on solidarity, free and open culture, is key to permeate and transform the various layers of governments, research institutions, and citizens-led initiatives. To address this, several examples are exposed offering a brief critical appraisal in the context of open science, a concept still in the making.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.252
Teacher spread0.245 · 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 designBench or experimental
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

Citations4
Published2021
Admission routes1
Has abstractyes

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