PhD Student Ambassadors: Partners in Promoting Graduate Research
Bibliographic record
Abstract
The aims of this research were to explore the experiences of staff and postgraduate students in an ambassador scheme, develop a model of partnering with postgraduate students in the administrative space, and consider implications for partnership initiatives. A qualitative case study was undertaken of a “Graduate Research Ambassador Scheme”, involving a dean employing two PhD students as paid ambassadors to help promote a vibrant graduate research culture. Research diaries were kept by each partner, regular research discussions occurred, and each partner wrote a reflective account of their experiences. These data were collaboratively analysed using a general inductive approach. All partners had very positive experiences, but there was some uncertainty regarding the nature of the role and some institutional challenges. A model of staff-student partnership within the administrative space was developed that included three main influences on effective partnerships: roles in partnership, structural characteristics, and personal characteristics. The model highlights the need for clear articulation of roles and tasks, the challenge of institutional cultures, and the way that resources, time, and space can either hinder or help partnerships. Personal characteristics such as trust, respect, and informal communication can significantly mitigate challenges and build fruitful partnerships.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".