Introducing Massively Open Online Papers (MOOPs)
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.
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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 | 0.050 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".