Bibliographic record
Abstract
In higher education, competing demands for accountability and innovation in the face of globalization, technology, and budget cuts cause us to consider how best to prepare learners who will learn for a lifetime. We contend that a shift in our understanding of curriculum design to accommodate learner-centeredness will provide the framework for preparing graduates for a lifetime of learning. Learner-centered curriculum proposes to create highly developed individuals, providing them the skills to continue creating learning experiences, digest current knowledge, and create new knowledge within the curriculum itself. Curriculum characteristics, as identified in the curriculum design project presented here, include content appropriate to the characteristics of a new society. It also includes all that is required of a curriculum in order for it to be transparent and easily understood as the scaffolding of learning. This definition of a learner-centered curriculum includes components that educators deem to be relevant and vital for students. It adds curriculum processes and required outcomes to prepare students for curriculum creation alongside educators.
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 | 0.047 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".