Bibliographic record
Abstract
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 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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".