What is Curriculum? Building a Broader Understanding of the Term
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
This paper is an attempt to come to a broader understanding of the term ‘curriculum.’ To that end, tens of curriculum definitions from language teaching and education literature were collected and analyzed using a proposed analytical formula. The ‘theme-rheme’ or ‘trunk-branch’ (as described and explained in the methodology) formula proposed here was utilized from Halliday’s (1985) systemic functional linguistics (SFL). This formula helps identify the part of the definition in which the topic is stated and the part of the definition in which Schwab’s (1973) commonplaces (main ideas) or some of them are discursively represented. This formula is not only helpful for analyzing definitions but also for writing definitions. Based on the analysis of definitions collected, the study defines curriculum as prescriptive content that illustrates what will be taught in a given educational program, who will teach, who will be taught, with what tools and in what context, with what effect, and how learners will be assessed.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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