Broadening the Definition of ‘Research Skills’ to Enhance Students’ Competence across Undergraduate and Master’s Programs
Why this work is in the frame
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Bibliographic record
Abstract
Undergraduate and master’s programs—thesis- or non-thesis-based—provide students with opportunities to develop research skills that vary depending on their degree requirements. However, there is a lack of clarity and consistency regarding the definition of a research skill and the components that are taught, practiced, and assessed. In response to this ambiguity, an environmental scan and a literature search were conducted to inform the creation of a comprehensive list of research skills that can be applied across programs and disciplines. Although published studies directly comparing research skills in thesis and non-thesis programs were limited, the specific skills reported in each program type were similar. This viewpoint article identifies the following seven research skills that were most frequently reported across both thesis and non-thesis programs: critical appraisal, information synthesis, decision making, problem solving, data collection, data analysis, and communication. When contextualized appropriately, these skills can be useful for a student during their academic program and are transferable across a range of future career pathways. Broadening the definition of “research skills” can inform curricular updates and program development, independent of their program type, to ensure that students are presented with explicit opportunities to develop the skills needed to succeed in their educational and occupational endeavours.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it