TITLE NATAL-74; Towards a Commo ogramming Language for
Bibliographic record
Abstract
NATAL-74 is a programing langU ge designed for Canadian computer aided learnin (CAL) programs. The language has two fundamental elements: the UNIT rovides the interface between the student and the subject matter, nd the PROCEDURE element embodies 'teaching strategy. Desirable fea ures of seve al programing langua44S have been adapted to Cope with a 'wide range of display equipment. A variety of computational capabilities, including a calculatio.I'mode, provide flexibility in use and response processing. A major goal o NATAL-74 is to provide an effeCtive means to exchange courseware proOr'ame: The implementation phase has initially used, the DEC,1,0 COmputer, but is working toward a high level of machine independence. Cooperation and continuing dialog between CAL users, ve dors and researchers is necessary to achieve a meaningful standarrd.J or a CAL Language. (CH)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.038 |
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".