Graduate Research Capabilities: A New Agenda for Research Supervisors
Bibliographic record
Abstract
There has been a conversation about university graduate employability within the Higher Education literature for some time (Cryer, 1997; Barrie, 2004, 2006, 2007; Murray, 2000; McAlpine, 2005). Within this, and often under the banner of questioning the relevance of the PhD (Murray, 2000), there have been discussions about the employability of research postgraduates. Both the broad discussion of graduate employment and the specific discussion of research degree graduate employment have produced an agenda of graduate research capabilities. Traditionally, assisting research higher degree (RHD) students with their career development has not been an articulated part of the research supervision process. However, the graduate research capabilities agenda has added a new element to the practices of research supervision, in that it brings with it a mandate for research graduates to be aware of the range of capabilities they have acquired through their research degree candidature and how these apply in the workforce. Additionally, there is an emphasis on preparing students for varied career paths rather than a traditional academic route (e.g., in industry or government). Supervisors have a vital role to play in assisting students with these important career development tasks. In this practice application brief we report on a strategy recently used at Queensland University of Technology (QUT) to assist supervisors understand their role in a student’s career development.
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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 | 0.087 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.035 |
| Scholarly communication | 0.035 | 0.044 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.024 | 0.035 |
| Insufficient payload (model declined to judge) | 0.013 | 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".