Bibliographic record
Abstract
Open science aims at the creation of public scientific goods by means of sharing outputs and widening and facilitating collaboration, in one or many of the different research stages. There are many beneficial aspects of open science that have been claimed in the literature, such us improving research efficiency, accelerating creativity, democratizing knowledge and empowering stakeholders. These claims are normally based on anecdotal experiences. In this paper we aim at organizing the extant literature on benefits of open science, in an attempt to build a bi-dimensional framework that relates characteristics of openness with benefits to be expected. The first dimension accounts for the characteristics of the collaboration, while the second for aspects of access to shared outputs. In the conclusion, we briefly illustrate our framework using evidence from four Argentinean open science initiatives.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".