“By June, Everyone Would Have Died”: Historicising Humour during the Covid-19 Pandemic in Ghana
Bibliographic record
Abstract
The impact of the COVID-19 pandemic on human interactions deeply affected the sense of humour of individuals. Using a social media research approach, this article analyses social media data in order to understand how social media users related with COVID-19. We sought to address the various events that generated humour among Ghanaians during the pandemic. The study reveals that the various instances of humour revolved around nose and/or face masks, terminologies and various social media posts. People neutralised their tensions and raised their humour with posts that flooded social media. This stemmed from the idea that people were mainly surprised at what they saw, watched or heard as they sought to release stress. We conclude that, owing partially to the creation of humour, the fear and tension associated with COVID-19 decreased with time among Ghanaians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".