A case analysis of political discourse ambivalence: Between the truth and falsity
Bibliographic record
Abstract
Many false statements in connection with COVID-19 have fueled a number of rumors and conspiracy theories in the world. Politicians tend to use complicated technical systems and information technologies in order to influence people’s consciousness, feelings and social behavior. Under the guise of taking care of people’s wellbeing they pursue their own objectives. The political leaders have challenged the world with their claims and political statements which hypocritically announced their striving to serve for the sake of the nations, but in fact demonstrating their strong will to benefit from the situation. However, their actions are not treated by people as aggression and don’t lead to open confrontation and aggravation of military and political relations. They paradoxically manage to balance between the truth and falsity, demonstrating ambivalence of what they state in their speeches and appeals to the nations. The basic methods of political discourse ambivalence analysis, used in the article, are: (a) fact-checking method, (b) scientific analysis of the evidence, (c) peer-reviewed studies and the others. There has been also used a method of logical comparison of three options of political discourse: Political Statement → Fact → Consequence. The analysis of mass media articles, devoted to Covid-19, has helped the author to systematize the elements of political discourse processing (the politicians’ statements for the good of the people) and political cognition (the actual meaning of those actions, which can potentially lead to confrontation between nations). The author is trying to find out the actual reasons of the growing gap between the governments and ordinary people, between nations in the world.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| 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; 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".