iCARE - Sampling-Based Differences in the Canadian Samples
Bibliographic record
Abstract
In early 2020, an international team of investigators, led by the Montreal Behavioural Medicine Centre (MBMC), launched a large survey-based project (the iCARE Study) aiming to track people’s experiences and behaviours during the COVID-19 pandemic across the world. As part of this endeavor, several sampling strategies were employed to recruit participants, but could be classified into two large categories. The first category regroups efforts (e.g., online advertising, snowball sampling through word-of-mouth) that recruits a convenience-based sample of unpaid volunteers to take the survey. The second category involves efforts to recruit more representative samples for given countries (e.g., using a polling firm that makes use of a large panel selected to match the wider population on key demographic characteristics). The current project aims to examine the degree to which samples produced by these two types of methods produce effects that are comparable/different from each other using Canadian data from the iCARE Study. A detailed preregistration file has been uploaded with this registration. This file outlines our hypotheses, data, and analytic plans.
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 | 0.063 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".