The partnership co-creation process: Conditions for success?
Bibliographic record
Abstract
Staff-student partnership activity continues to increase across the higher education sector, expanding to encompass a broad range of initiatives. Numerous frameworks and typologies have been proposed to help organise the literature and facilitate comparisons among different types of partnerships. The research reported here draws on a case study of a quality-enhancement staff-student partnership to identify the stages of the partnership co-creation process. It argues that the establishment of partnership values is intertwined with the stages of the co-creation process and is critical to the partnership’s success. This research contributes to practice and the literature by offering a practical approach to managing a staff-student partnership, adding to work on quality enhancement partnerships, and extending prior work evaluating partnership activity from the perspectives of multiple stakeholders.
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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 | 0.051 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.037 | 0.028 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".