Bibliographic record
Abstract
Background: This article examines Implementing New Knowledge Environments’ (INKE) experiences as a mature, large-scale collaboration working with academic and non-academic partners and provides some insight into best practices. It looks at the sixth year of funded research.Analysis: The study uses semi-structured interviews with questions focused on the nature of collaboration with selected members of the INKE research team. Data analysis employs a grounded theory approach.Conclusion and implication: The interviewees found the experience of collaborating within INKE to be positive with some ongoing challenges. The team is winding down as it moves into the final year of funded research. This suggests an arc of collaboration, with intensity of collaboration building from the first year to the most intensive time in the middle years and then winding down in the last years of grant funding. This article contributes to those lessons about collaboration by exploring the lived experience of a long-term, large-scale research project.
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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.021 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".