Collective remembering and future forecasting during the COVID-19 pandemic: How the impact of COVID-19 affected the themes and phenomenology of global and national memories across 15 countries
Bibliographic record
Abstract
The COVID-19 pandemic created a unique set of circumstances in which to investigate collective memory and future simulations of events reported during the onset of a potentially historic event. Between early April and late June 2020, we asked over 4,000 individuals from 15 countries across four continents to report on remarkable (a) national and (b) global events that (i) had happened since the first cases of COVID-19 were reported, and (ii) they expected to happen in the future. Whereas themes of infections, lockdown, and politics dominated global and national past events in most countries, themes of economy, a second wave, and lockdown dominated future events. The themes and phenomenological characteristics of the events differed based on contextual group factors. First, across all conditions, the event themes differed to a small yet significant degree depending on the severity of the pandemic and stringency of governmental response at the national level. Second, participants reported national events as less negative and more vivid than global events, and group differences in emotional valence were largest for future events. This research demonstrates that even during the early stages of the pandemic, themes relating to its onset and course were shared across many countries, thus providing preliminary evidence for the emergence of collective memories of this event as it was occurring. Current findings provide a profile of past and future collective events from the early stages of the ongoing pandemic, and factors accounting for the consistencies and differences in event representations across 15 countries are discussed.
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 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".