E-science, E-research and E-learning: New Perspectives for Graduate Studies
Bibliographic record
Abstract
Abstract: The quality of education for doctoral students is closely linked to the quality of the research they undertake. Benefiting from technological advances, new distributed and collaborative research practices can be witnessed. The concept of e-science has emerged and evolved to the concept of e-research. These concepts bring about a new research philosophy and the notion of collaboratory. Our research project aims to develop a renewed doctoral training approach and to facilitate researchers ' adoption of new research practices which will then be reinvested in the training of future researchers. Three general objectives have been defined: the rethinking of instructional model and objectives of doctoral training, the development of new technologyenhanced research practices and their reinvestment in graduate studies, and the development of approaches and methodological tools to support the preceding objectives. This paper presents the results of the first of a three year project. Context Most current research agrees on the pedagogical factors responsible for student loss and the excessively long duration of doctoral studies. Studies which investigate students ’ opinions indicate that the academic experience has a significant influence on their decision to abandon their studies (Gemme & Gingras 2006; Golde & Dore 2001).
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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 | 0.021 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".