Reflecting on a student-staff partnership
Bibliographic record
Abstract
This reflective piece, delves into the narratives and experiences of students Chris, Aoife, Jordan, and Karolina; participants in a student-staff partnership aimed at enhancing the first-year undergraduate experience. This student-staff partnership came to fruition through a fellowship awarded by the National Technological University TransfOrmation for Recovery and Resilience project (N-TUTORR), an innovative collaboration across the Technological Higher Education sector to transform the student experience.. The partnership project was titled; ‘SETU Includes U: ‘Facilitating Friendships’ Induction for Year 1 Students’. The students-staff partnership team worked together to design and deliver a sustainable induction day framework and these induction days were ran by the team in the Department of Nursing and Health Care at the South East Technological University (SETU). To capture the lived experiences of the students involved in the partnership process, an adapted version of the Gibbs Reflective Cycle has been used as a framework for this reflectice piece. Their accounts provide a multifaceted view of the complexities of the partnership process, from initial apprehensions to eventual triumphs, and offers valuable insights to the lived experiences regarding student staff partnerships in higher education, with implications for future practice.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.005 | 0.012 |
| 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".