The ROSie (Research, Outreach and Study) Project
Bibliographic record
Abstract
The COVID-19 pandemic dealt a blow to developmental psychologists, given their reliance on recruiting local community members to participate in in-lab experiments. As a remedy, we partnered with a software company and developed a novel application for mobile computing devices (smartphones and tablets) that will enable families across Canada to participate in online studies with low or no mediation. To validate this approach, we investigate whether various measures of children’s cognitive development (i.e., cognitive flexibility/inhibitory control, Theory of Mind, working memory) differed across three methods of assessment using the app: in-person, video-conference monitored, and unmonitored. Throughout this project, we aim to identify critical factors that must be in place to enable high quality data collection. This will provide a means for recruiting larger samples than researchers typically have access to. It will also deliver research studies on common household devices that are available at any time and place, increasing access to a much broader and diverse range of participants. Ultimately, it will allow us to investigate overlooked factors (e.g., gender, SES) important for cognitive development.
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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.018 |
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".