The “DEFT” Project
Bibliographic record
Abstract
The student experience of university is shaped in part by what lecturers – who are significant figures and role models – expect of students. In particular, what is the type and quality of thinking that we expect students to be engaging in? The DEFT (Developing Expertise Fostering Thinking) Project is a Professional Development Project being co-created and undertaken by a group of university lecturers across diverse institutions and contexts. We are interested in how our students think, what they think about, and how we can be more effective and precise in fostering thinking amongst our cohorts. This work builds on scholarly literature on the nature of expertise – the quality we want to develop. In particular we draw upon the PhD of one of our number, Peter Ellerton, who leads the University of Queensland Critical Thinking Project. We also draw upon inspiration from considerations as to the nature of mathematical thinking. Our work is strongly but not exclusively contextualized within the Mathematical Sciences. The approach is a praxis approach, drawing upon the growing practical expertise of each of us as educators working within Universities in Australia and Canada. In working together across diverse institutions and time zones, we are utilising videoconferencing for regular meetings in which we discuss our work and approaches. We discuss our current practices, and reflect upon these in the light of our theoretical framing. A keystone of the work is the assumption that (both for ourselves as educators and our students as developing professionals in diverse field) the development of expertise relies upon building understanding and conceptual schema through (deliberate) practice.
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.014 |
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".