“INKE-cubating” Research Networks, Projects, and Partnerships: Reflections on INKE’s Fifth Year
Bibliographic record
Abstract
Humanists are participating in collaborations with others in the academy and beyond to explore increasingly complex research questions with technologically oriented methodologies and access to advice, mentoring, technology, knowledge, and funds. Although these projects have clear benefits for all those involved, these collaborations are not without their challenges. Such styles of partnership tend to be more common on the science side of campus. As a result, little is understood about the ways that they might work within the humanities and the range of benefits that can be available to members within a mature collaboration. To this end, this paper will examine the experiences of Implementing New Knowledge Environments (INKE) as a mature, large-scale collaboration working with academic and non-academic partners and will provide some insight into best practices.
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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 | 0.040 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.040 |
| Scholarly communication | 0.036 | 0.023 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.008 | 0.021 |
| 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".