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Record W3134845612 · doi:10.1111/jtsb.12264

The construction of social reality as a process of representational naturalization. The case of the social representation of drugs

2021· article· en· W3134845612 on OpenAlexaff
Lilian Negura, Nathalie Plante

Bibliographic record

VenueJournal for the Theory of Social Behaviour · 2021
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNaturalizationObjectificationSocial realityEpistemologySociologyProcess (computing)Object (grammar)Representation (politics)Social representationSocial constructionismStructuringPsychologySocial psychologySocial sciencePolitical scienceComputer scienceLawArtificial intelligencePoliticsPhilosophy

Abstract

fetched live from OpenAlex

Abstract This theoretical paper explores the role of social representations in the construction of social reality. Even though this question has been the subject of many debates, the actual processes and mechanisms through which social representations contribute to the construction of social reality have rarely been explored. Citing key works on the topic, the paper explores the role of the genesis of social representations in this process. Each stage of the objectification of social representations (selective construction, structuring schematization, and naturalization) is examined in detail. A more in‐depth analysis of the naturalization process is provided by dividing it into four phases: (1) recognition; (2) elimination of contradictions; (3) instrumental use; and (4) validation through experience. These phases are illustrated using the example of the construction of the social object of drugs in our contemporary society. The present examination of the naturalization process in relation to drugs reveals the mechanisms through which the reality of drugs as a social problem has been constructed and reproduced in our society.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.082
GPT teacher head0.500
Teacher spread0.418 · 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.

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

Citations15
Published2021
Admission routes1
Has abstractyes

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