Frameworks and freedoms: Supervising research learning and the undergraduate dissertation
Bibliographic record
Abstract
Much current debate about undergraduate student research involves a focus on ‘students as partners’ and co-constructors of knowledge (Healey, Flint & Harrington 2014, 2016). This debate reveals interesting tensions between student freedom and the role of structuring frameworks. Undergraduate lecturers and research supervisors might feel we are in a quandary concerning how far we can help manage a balance between supportive frameworks and the independence that student researchers need to develop. Will the use of the Research Skill Development (Willison & O’Regan, 2006/2018) framework and other frameworks at every step of the undergraduate research journey form a constraint, or an essential scaffold? This paper considers frameworks, scaffolds and the need for freedom and creative co-construction of knowledge to enable successful undergraduate research within the context of final year research and writing at undergraduate third year (UK), honours (Australia) or senior/fourth year (US and Canada).
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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 | 0.114 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.081 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".