Partnering for Impact: A Grand Challenge and Design for Co-Creating a Just, Resilient and Flourishing Society
Bibliographic record
Abstract
In this paper I elaborate on the design and dimensions of interorganisational collaborations particularly when the purpose of connecting is the co-creation of knowledge for impact. I extend recent accounts of co-creating knowledge and explain why co-creation is integral to the “common good” logic especially when the focus of partnering for impact embraces the marked improvements in action that constitutes the impact of collaboration. I then elaborate on “partnering for impact” as a collaborative design and explicate the axiology that this mode of co-creation calls for, marked by a fresh perspective on inclusiveness founded on isotimia and philotimia . I illustrate the manifestation of these dimensions in the GNOSIS approach of co-creating impact through the embeddedness of “re-search” as a common practice. I conclude by inviting greater reflexivity in the relationship between science and society when partnering for impact is intended to co-create a just, resilient and flourishing society.
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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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".