Systematizing benefits of open science practices
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
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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.012 | 0.103 |
| Open science | 0.010 | 0.004 |
| 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