Evidence-Based Development of Doctoral Education: The Landscape of Doctoral Students' Experience Research
Bibliographic record
Abstract
Regular surveys of doctoral students on their career trajectories, satisfaction with the program and the learning process, with the organization of supervision, etc. are widespread in leading foreign universities. The results of such surveys are used to improve programs and assess the effect of the introduced measures. In Russia, however, there is a lack of empirical data on the doctoral students’ experience, which makes it impossible to identify and address the reasons for the low performance of the Russian doctoral education observed over the past few years. To support the discussion about the need for such monitoring surveys in Russia, this article presents the results of an analysis of open information from the websites of about 150 foreign institutions that organize doctoral student surveys at the national, cross-university, and institutional levels. The presented review shows how actively doctoral education data collection takes place in the USA, Canada, Australia, and the UK, where there are one or more large-scale projects stimulating the collection of data from several universities, and how the results of such research are used by universities, employers, and applicants. On the example of topics related to the motivation for entering doctoral programs, to the career trajectories of doctoral students, and to the organization of supervision, it is discussed how the described research practices can be used for the evidence-based development of Russian doctoral education.
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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 | 0.424 | 0.513 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".