COVID-19 illegal social gatherings: Predicting rule compliance from autonomous and controlled forms of motivation.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study was to identify predictors of rule compliance regarding private gatherings during the 2020 Christmas holidays in the province of Quebec (Canada), where gatherings were ruled as illegal, with few exceptions. We used the self-determination theory framework to predict rule compliance as a function of autonomous, controlled-approach and controlled-avoidance motivations. Moreover, we measured psychological distress among participants as well as anxiety of COVID-19 exposure. Motivation and psychological distress measures were taken a couple of days prior to the holiday period, whereas rule compliance was measured approximately 10 days later, in early January. A total of 1332 individuals filled the first online survey and 627 completed the follow-up measure. The factorial structure of the motivational instrument was supported. Rule compliance was predicted positively by autonomous motivation, but negatively by controlled-avoidance motivation. Controlled approach was not a significant predictor of rule compliance. These results show that approach and avoidance orientations in controlled motivation have distinct predictive power, which has implications for policy-enforcing by governments. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 it