A student perspective on Big Team Science: Pathways to open science
Bibliographic record
Abstract
As Big Team Science (BTS) reshapes how research is conducted, it is also transforming how students learn to do science. While BTS has been widely recognized for its methodological contributions, its pedagogical potential—particularly as a gateway to Open Science—remains under-examined. This student-led lightning talk uses BTS as a case study to explore how students can be meaningfully introduced to Open Science practices and ultimately thrive in applying them. We reflect on BTS as a training context where participation is distributed, learning is experiential, and collaboration is foundational. Drawing on firsthand experience with ManyManys 1—a large-scale collaboration investigating comparative cognition across animal taxa—we identify five key ways BTS is changing the future of research training: it expands access through global networks, invites students to contribute to shaping research decisions, exposes them to more inclusive and representative forms of science, fosters reflection on the conceptual foundations of scientific practice, and accelerates the development of Open Science habits through active, hands-on participation. BTS offers more than technical skill-building; it supports identity formation, critical thinking, and a deeper sense of belonging grounded in the ethos of Open Science.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.008 | 0.015 |
| 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, 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".