The governance of open science: A comparative analysis of two open science consortia
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
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.
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.
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 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 | 0.041 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.054 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.075 | 0.066 |
| Research integrity | 0.000 | 0.000 |
| 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