Casting a Wider Net: Deepening Scholarship by Changing Theories
Bibliographic record
Abstract
Research questions are shaped, explicitly or implicitly, by the theories we bring to bear upon our scholarship. Broadening our theoretical perspectives allows us to frame richer, deeper questions about the teaching and learning happening in our classrooms. This paper explicates the research implications of three broad theories of learning (constructivist, socio-cultural, and complexivist), exploring what scholarship framed by each theory might look like and some of the strengths and limitations of each framework. The authors use their experience engaging in research on teaching and learning in an undergraduate interdisciplinary science program to illustrate the argument that changing theories can help improve the scholarship and practice of teaching and learning in higher education.
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 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 | 0.123 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.011 | 0.116 |
| Scholarly communication | 0.040 | 0.093 |
| Open science | 0.008 | 0.036 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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".