Integrating open science in the teaching of cognitive research methods: Comparing virtual vs. face-to-face delivery
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
Openness, transparency, and reproducibility are widely accepted as fundamental aspects of scientific practice. However, a growing body of evidence suggests these features are not readily adopted in the daily practice of most scientists. The Centre for Open Science has championed efforts for systemic change in the scientific process, endorsing practices such as preregistration and open sharing of data and experimental materials. In an effort to inculcate these practices early in training, we integrated several key components of open science practice into an undergraduate research methods course in the cognitive sciences. In the first iteration of the course done in the traditional face-to-face format, students were divided into research teams: each with the goal of carrying out a replication experiment related to the topics in the course. Teams completed a preregistration exercise, and importantly, were encouraged to consider a priori the criteria for a successful replication. They were also required to collect and analyze data, prepare manuscripts, and disseminate their findings in poster symposia and oral presentations. In two subsequent iterations of the course, the COVID-19 pandemic forced the course into an online, asynchronous format. Whereas the course deliverables were modified substantially to suit the new format of the course, the learning objectives remained the same. Students independently conceptualized a replication experiment of their own choice based on their interests in the course material. Considerable flexibility was built into the capstone projects in order to empower students to focus on work they found engaging. Students were encouraged to focus on the theoretical motivations for replicating their study of choice, based on consensus (or lack theoreof) of a literature review, as well as on the methodological and analytical aspects of their replication, guided by preregistration templates. Critical appraisal of the goals and implementation of the course across formats are discussed.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchOpen science Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | Open science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.195 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.027 | 0.075 |
| Research integrity | 0.000 | 0.003 |
| 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 it