The governance of open science: A comparative analysis of two open science consortia
Bibliographic record
Abstract
Recent open science efforts recognize that the efficient, credible, and transparent development of scientific knowledge relies on the capacity to verify and reuse the “intermediate resources” employed throughout the research process, including data, computer code, and other research material. Prior research has shown that the disclosure of such resources is often hindered by the incentives and disincentives perceived by individual scientists. Beyond the level of individual incentives, however, the sharing of intermediate resources is obstructed by the governance norms that inform these incentives in the first place, such as the norms of authorship and evaluation. Thus, our central research question asks how the limitations of the established norms of authorship and evaluation are addressed at the organizational level within open science consortia that are premised on the sharing of intermediate resources. Drawing on qualitative methods, we present an in-depth comparative analysis of two open science consortia–the Canadian Open Neuroscience Platform (CONP) and The Cancer Genome Atlas (TCGA)–that illustrates how the limitations of the established norms of authorship and evaluation are navigated in brain and cancer research, respectively. Our findings show that the governance mechanisms designed and implemented in CONP and TCGA reflect two distinct forms of governance, one distributed and the other layered, which are characterized by different understandings of scientific authorship and evaluation. Our study thus contributes to ongoing debates on open science and the governance of scientific collaboration by shedding light on the relationship between governance forms and variable conceptions of authorship and evaluation. • The effectiveness of resource sharing depends on how open science consortia are organized and governed. • The governance of open science has implications for authorship norms. • The governance of open science has implications for evaluation norms.
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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 | Open scienceMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | MetaresearchOpen science Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.061 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".