Research Practice in Research Assistantships: Introducing the Special Issue on Research Assistantships
Bibliographic record
Abstract
The idea for this special issue came from our mutual interest in research education and the development of future researchers. Our shared program of research has led us to discover the potentials, complexities, and dilemmas associated with research assistantships where newcomers assist more experienced researchers to conduct research projects. We considered a wide range of proposals and papers addressing different aspects of research assistantships. The resulting collection includes self-studies and analyses of others, as well as policy reviews and recommendations. The pieces consider research assistantships involving bachelor's, master's, and doctoral students in four different countries (Canada, Denmark, South Africa, United States) and across a range of disciplines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.184 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.010 | 0.002 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 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; both teacher heads 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".