Integrating open science in the teaching of cognitive research methods: Comparing virtual vs. face-to-face delivery
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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How this classification was reachedexpand
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 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 | 0.077 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".