Content. While we go bigger and beyonder, who holds down the fort for us? A perspective on modeling curricula to promote maintenance and strategic enhancement.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As reflected by a growing consensus within the education community, content can only take our students so far (Deller et al., 2015). We now aim for discipline-specific as well discipline-independent higher order and transferable outcomes that promise to serve our students and scientific community bigger and better. While we renovate our courses and curricula to achieve these goals, how do we maintain curricular infrastructural integrity? How to implement these improvements in a manner that sustains curricular quality assurance, accountability, accessibility, and strategic spending?This session shares and reflects on a curricular modeling perspective that can hold down our curricular fort while we aim bigger and beyonder. It emphasizes international effort promoting the development of program-level learning outcomes (PLLOs) at the post secondary education level (Goff et al., 2015). It also extends the PLLO model to embrace discipline-specific and –independent higher order and transferable outcomes so that curricula can evolve nationally and internationally in a calculated and grounded manner.\nDeller, F., Brumwell , S., and MacFarlane, A. (2015). The Language of Learning Outcomes: Definitions and Assessments (Higher Education Quality Council of Ontario).\nGoff, L., Potter, M.K., Pierre, E., Carey, T., Gullage, A., Kustra, E., Lee, R., Lopes, V., Marshall, L., Martin, L., et al. (2015). Learning Outcomes Assessment: A practitioner's Handbook (Higher Education Quality Council of Ontario (HEQCO)).
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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