Reflections on deep academic–practitioner partnering for generative societal impact
Bibliographic record
Abstract
While academics increasingly point to the value of engaged scholarship, we describe a more extreme form which we label as "deep partnering"-a long-term, holistic, and dynamic collaboration between academics and practitioners to achieve shared goals. Deep partnering involves interdependent and evolving interactions between academics and practitioners over an extended time period. While such relationships enable generative impact on important issues, these relationships remain challenging as academics spend time in the practitioners' complex worlds, surfacing paradoxes due to the partners' conflicting roles, time horizons, and goals, as well as uncertainty in the partnership's evolution. In this essay, we reflect on our experiences working closely with practitioners on a program of research over more than a decade in order to expand on a deep partnering approach, including the paradoxes and emotional discomfort it surfaces, and we identify practices to navigate these paradoxes.
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 | 0.058 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.058 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.004 | 0.046 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".