Competence, Resilience, and Adaptability With and Without Learning Augmentation (CRAWWLA) - final report
Bibliographic record
Abstract
Our original intent was to engage Calgary teachers with issues associated with resources that were added or absent from their teaching situations and how they adjusted to these changing circumstances. From that we had intended to develop resources to help teachers make better use of these situations when they were ‘with and without’. This is not what we ended up doing. In the first year of the project we conducted a series of interviews with teachers and learners in medicine, veterinary medicine, and nursing and were taken aback by how little engaged they were with the issues of with and without in their teaching practice. They acknowledged that there were issues and opportunities but responded to them in a very normative way. It became clear that, while this is a common issue in teaching and learning, teachers are used to ‘rolling with the punches’ and taking a relatively passive and at times fatalistic stance on this issue. We also tried to conduct a scoping review, but were unable to identify a coherent body of literature that we could synthesize. We clearly needed a new plan Working with my two collaborators (David Topps – a family medicine doctor, education scholar, and long-term collaborator – and Michelle Cullen – an RN nurse educator) and in agreement with the TI we changed our plans in Phase 2 to focus on four subprojects
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.085 | 0.028 |
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".