Social Distancing During the Pandemic: A Semiotic Approach to Organizational Response through Commercial Branding
Bibliographic record
Abstract
Social distancing is one of the most practical and most widely emphasized non-pharmaceutical interventions recommended globally in response to the COVID-19 pandemic. Even though its efficacy remains debatable, social distancing continues to be advocated as a strategy to “flatten the curve” by reducing individual infections. This study aims to decode the semiotics of COVID-19 pandemic from one side and to show how commercial branding transformations took place from another. Global organizations have aligned themselves with social distancing precautions by adapting their commercial branding for visual messaging. This study takes a semiotic approach to the commercial branding of companies that could transform their branding during the pandemic and those that did. The two questions addressed by the study are: (1) How did commercial branding transform during the COVID-19 pandemic, and (2) what semiotic codes are evident in these transformations? The findings show that organizational branding was separated or reworded or took a two-pronged approach (combining rewording and transformed images).
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".