The construction of social reality as a process of representational naturalization. The case of the social representation of drugs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.145 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".