Bibliographic record
Abstract
Many academic teams and granting agencies undergo a process of reflection at the completion of research projects to understand lessons learned and develop best practice guidelines. Generally completed at the project’s end, these reviews focus on the actual research work accomplished with little discussion of the work relationships and process involved. As a result, some hard-earned lessons are forgotten or minimized through the passage of time. Additional learning about the nature of collaboration may be gained if this type of reflection occurs during the project’s life. Building on earlier examinations of INKE, this paper contributes to that discussion with an exploration of seventh and final year of a large-scale research project.Implementing New Knowledge Environment (INKE) serves as a case study for this research. Members of the administrative team, researchers, postdoctoral fellows, graduate research assistants, and others are asked about their experiences collaborating within INKE on an annual basis in order to understand the nature of collaboration and ways that it may change over the life of a long-term grant. Interviewees continue to outline benefits for collaboration within INKE while admitting that there continue to be challenges. They also outline several lessons learned which will be applied to the next 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 | 0.078 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.026 | 0.035 |
| Scholarly communication | 0.033 | 0.040 |
| Open science | 0.006 | 0.049 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".