Bibliographic record
Abstract
The purpose of this study is to explore how beliefs about the COVID-19 pandemic change over time across the world. Specifically, we plan to test whether participants exhibit hindsight bias for the outcomes of COVID-19. Hindsight bias is the tendency to see the outcome of an event as more foreseeable once the outcome is known than before the outcome was known. It is important to study the effects of hindsight bias for this global event because it can affect people's evaluations of the appropriateness of the responses to COVID-19 and whether they will adjust their own responses accordingly. We will recruit participants primarily through Amazon Mechanical Turk (Mturk), an online research pool through which participants volunteer to participate in research for pay. We may also recruit participants from local university research pools in British Columbia, Canada as well as on Reddit. When participants sign up for the study, they will receive a link to a survey, which is hosted through the Qualtrics website. The survey will ask several questions about the foreseeability of COVID-19 (e.g., “how predictable was the pandemic?”) as well as various COVID-19 outcomes. Approximately 8-10 weeks later, we will send participants a link to a follow-up questionnaire, which will ask similar questions. For the hindsight bias questions, we will provide information about the current state of affairs (e.g., economic/social impact) for half the items and ask participants to ignore their knowledge of the current state of affairs and recall their original answers to the questionnaire. They will not receive any outcome information for the remaining questions. We will also send the questionnaire to a new group of participants 8-10 weeks after we administer the first survey that asks them to estimate how their peers would have responded to the questions two months earlier. This is another way to investigate how outcome information can change how we evaluate others’ responses. All questionnaires and materials will be available on the project page on OSF, or by request from the authors.
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 | 0.019 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".