Can Methodological Requirements Be Fulfilled When Studying Concealed or Unethical Research Objects? The Case of Astroturfing (De La Difficile Application Des Standards Méthodologiques Aux Objets De Recherche Camouflés Ou À L’Éthique Discutable: Le Cas De L’Astroturfing)
Bibliographic record
Abstract
English Abstract: Scientific soundness is key in the development of research designs. Methodological choices bear the responsibility to demonstrate its obtainment. However, it is challenging to abide by these standards while dealing with hidden, masked or unethical objects. In this article, we share the various strategies employed to aim at a sound scientific process in spite of astroturfing’s characteristics and of the methodological orientations it dictates. Facing the dilemma between the importance of scientific value and the richness of inductive and exploratory approaches, we question the influence of positivist research standards in communication studies. We fear these requirements may limit their development.French Abstract: La quete de la scientificite est au cœur du design d’une recherche et les choix methodologiques en sont majoritairement tributaires. Toutefois, les objets de recherche masques ou a l’ethique discutable posent des defis particuliers. Cet article expose les strategies deployees en depit des caracteristiques de l’astroturfing et des orientations methodologiques qu’il impose. Le dilemme entre les exigences de la scientificite et la richesse des approches inductives et exploratoires, en fut la toile de fond. Nous terminons en questionnant l’influence exercee par le paradigme positiviste sur les etudes en communication, craignant qu’il en restreigne le developpement plutot que de le stimuler.
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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 | 0.540 | 0.602 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".