Bibliographic record
Abstract
The article is an excerpt from a wider research project carried out by the author in October– December 2020, concerning advertising materials used by WHO and selected countries (Poland, Australia, Canada, New Zealand and South Africa) in social campaigns during the SARS-CoV-2 pandemic. This text presents one case study – a campaign used in Poland, comparing its messages with WHO advertising materials. The main thesis was taken from the thought of Ivan Krastev, who claims that the pandemic made everyone realize that all people are inhabitants of “One World” in the face of a global threat. The entire study used the triangulation of two research methods – case study and compositional interpretation by Gillian Rose. Roman Jakobson’s model of linguistic communication was used to examine the verbal layer of messages. In the linguistic layer of the messages, their considerable persuasiveness was assumed. In the visual layer, due to the simplicity of the form, it is limited to the compositional modality, with particular emphasis on colors and iconic signs. The text shows how important a role in communication, especially in times of a pandemic, is played by social advertising campaigns. Paradoxically, a pandemic that threatens humanity may also open up new, comparative areas of research on the effectiveness of mass communication means used in some countries, which can be successfully used in others.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".