Introducing Massively Open Online Papers (MOOPs)
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
An enormous wealth of digital tools now exists for collaborating on scholarly research projects. In particular, it is now possible to collaboratively author research articles in an openly participatory and dynamic format. Here we describe and provide recommendations for a more open process of digital collaboration, and discuss the potential issues and pitfalls that come with managing large and diverse authoring communities. We summarize our personal experiences in a form of ‘ten simple recommendations’. Typically, these collaborative, online projects lead to the production of what we here introduce as Massively Open Online Papers (MOOPs). We consider a MOOP to be distinct from a ‘traditional’ collaborative article in that it is defined by an openly participatory process, not bound within the constraints of a predefined contributors list. This is a method of organised creativity designed for the efficient generation and capture of ideas in order to produce new knowledge. Given the diversity of potential authors and projects that can be brought into this process, we do not expect that these tips will address every possible project. Rather, these tips are based on our own experiences and will be useful when different groups and communities can uptake different elements into their own workflows. We believe that creating inclusive, interdisciplinary, and dynamic environments is ultimately good for science, providing a way to exchange knowledge and ideas as a community. We hope that these Recommendations will prove useful for others who might wish to explore this space.
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 | 0.003 | 0.019 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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