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
Much attention has been paid in recent years to finding more flexible and less prescriptive approaches to the design of instruction than those put forward during the latter part of the twentieth century. A view of instruction as causal and largely behaviouristic has given way to one that is guided and primarily constructivist. In his current research, David Merrill outlines five fundamental principles of instruction which have broad implications for teaching computer science (CS). These five principles are: 1) solving real-world problems, 2) activating existing knowledge to build new knowledge, 3) demonstrating new knowledge to the learner, 4) allowing learners to apply new knowledge, and 5) integrating knowledge into the learner’s world. The following paper describes these principles and discusses how they related to instruction in CS. M. David Merrill’s career in instructional technology, with a career has spanned 40 years, and include numerous significant contributions to the field. He is probably best known for his Component Display Theory [1]. In the 1990’s he was one of the foremost
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 | 0.000 |
| Open science | 0.001 | 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