The Queen’s University Administrative Internship (QUAI): A First-in-Canada Experiential Learning Opportunity for Ph.D. Students
Bibliographic record
Abstract
The scarcity of tenure-track positions for Ph.D. students has resulted in many turning their interest and attention to alternative career opportunities within academia. It is here that universities can play a role by providing resources and developing career opportunities that would benefit from such highly skilled graduates. Such career prospects can range from project grant management to research institute administration. However, the visibility of these career pathways is either not fully apparent or not always encouraged. Additionally, even for individuals who have secured tenure-stream positions, there is often little knowledge about how university administration functions. This situation can leave faculty, staff, and students without the necessary awareness or understanding of university environments that support education and research success. Queen’s University is well-positioned to provide professional development and experiential learning opportunities to Ph.D. students. In doing so, Queen’s University could address both the need for – and growing interest in – diversified employment opportunities. We propose Queen’s University augment existing professional development opportunities by implementing the Queen’s University Administrative Internship (QUAI). This internship program would match Ph.D. students with administrative units and/or research institutes to both highlight alternative careers pathways in academia and broaden the scope for employment options post-graduation.To our knowledge, this program with semester-long placements would not only be the first at Queen’s University but also the first in Canada. The goal of QUAI is for interns to gain an in-depth knowledge of university administration while learning new skills and strengthening networks. Interns will enhance their professional profile as they prepare for academic, research, or administration positions. The program would also supplement their professional skills portfolio by giving them institutional understanding when applying for careers beyond traditional academia. It would allow interns to learn about the university administrator profession. Our strategy will leverage Queen’s University administrative units across campus as experiential training grounds for the next generation of research administrators to achieve these goals.We propose to structure the QUAI into the following components: 1) work shadowing with senior leaders, 2) mentoring with senior leaders in host units or other units. and 3) professional skills development by working on projects, attending workshops, and networking with peers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".