Modelling Open Social Scholarship Within the INKE Community
Bibliographic record
Abstract
Given the current state of digital technology, there is a clear opportunity to revamp scholarly communication into a multi-faceted, open system that integrates and takes advantage of the near-ubiquitous global network. In doing so, the values of collaboration, sharing, and transparency inherent to open social scholarship can be integrated into knowledge dissemination methods. The Implementing New Knowledge Environments (INKE) community is currently organized around the idea of open social scholarship, but putting this into practice will involve assessing and revising INKE’s own scholarly communication processes. In this paper, we explore the current state of open access to academic research and ruminate on next steps, beyond open access. We consider the role of collaboration in contemporary academic practice, and the importance of transparency in regards to multiplayer work. Further, we examine the standard scholarly communication model, especially as it pertains to INKE. Finally, we make recommendations and suggest alternatives for transforming our stock scholarly communication models into open social scholarship practices.
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.
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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".