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Record W4393375700 · doi:10.21747/21836671/pag2024a3

Dinâmicas Da Desinformação

2024· article· en· W4393375700 on OpenAlexaff

Bibliographic record

VenuePáginas a&b Arquivos & Bibliotecas · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsGibson Energy (Canada)
Fundersnot available
KeywordsGeology

Abstract

fetched live from OpenAlex

The contemporary information scenario has been marked by new conditions of production, circulation and use of information, in which the wide circulation of partially or completely false and misleading content has acquired great importance. This phenomenon has been described and analyzed using different concepts, such as disinformation, infodemic, post-truth, fake news and others. Many analyzes have been carried out with the aim of identifying their causes, characteristics and consequences, as well as their constitutiveelements, modes of manifestation and interfaces with the different areas of human life (political, economic, social, health, etc.), generating a large accumulation of scientific knowledge in recent years. A challenge that has arisen recently is precisely to systematize such knowledge. The objective of the present work, therefore, is to systematize such analyzes from a specific perspective: to identify the dynamics of such phenomena, based on their functional causalities. To this end, the work of Gibson Burrell and Gareth Morgan was used as a reference, who applied a model of four sociological paradigms to research in organizations. This model foresees four paradigms: functionalist, interpretative, radical humanist and radical structuralist. After presenting these paradigms, their application is made in studies on the phenomena of disinformation. The result of this analysis makes it possible to highlight the different dynamics of disinformation in human and social life: dysfunctions of knowledge production institutions, intentional actions to produce misinformation, everyday social construction through the valorization of certain contents, and the positioning of subjects in relation to disinformation strategies. This diversity of aspects act in complementarity, and each of them requires specific actions to combat their harmful effects.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.052
GPT teacher head0.363
Teacher spread0.311 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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