I Stayed for the Community: Collaboration and Community in an Open Social Scholarship Research Project
Bibliographic record
Abstract
Open social scholarship implies a community of academic specialists and non-specialists working together to create, disseminate, and use research to ensure that it is engaged in broader contexts than initially envisioned. But what are the best ways to do this? The Implementing New Knowledge Environment (INKE) project on open social scholarship is working to answer this question with a collaborative team of academic and academic-adjacent researchers and partners. But this raises questions in its own right – how can researchers and partners, with differing organizational cultures, objectives and goals, and expertise, work together to further open social scholarship? Continuing research on collaboration from the first INKE project on electronic books and reading, this paper examines the nature of collaboration with INKE’s new focus on open social scholarship. Through yearly interviews of team members, it explores the nature of collaboration, its advantages and disadvantages, and measures of success. This paper will explore the collaboration’s first year of research. It will also include some reflection on collaboration in the age of COVID.
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 | 0.071 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.045 | 0.040 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.003 | 0.049 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".