Bibliographic record
Abstract
It can be a tad sobering to re-read for the first time in over twenty years something written then in a hurry.I had been assigned to teach my first curriculum course at Simon Fraser University and thought it might be a good idea to find out what "curriculum" was.Many of the people I talked to in the field seemed to have rather divergent views, and some cheerfully admitted they had no idea.Some suggested I look at the kinds of books used by curriculum professors in their courses and infer what the field covered from that.And, of course, I studied the big Curriculum textbooks.None of this made things much clearer, oddly enough.I also wanted to start my class off with something that would clarify what curriculum was supposed to be about for the students.Not being able to find anything that seemed to me adequate at the time, I wrote "What is Curriculum?" for my students, and later sent it off to the editor of Curriculum Inquiry, who, I tend to think looking at it now, was unduly kind in printing it.A number of issues come to mind on re-reading.First, a rather uncomfortable awareness of showing off with the Latin quotes.I had been then closer to my schooldays, when Father Paul had tried to persuade us that Latin was our mother tongue, and this English vulgate we slopped around in was no language in which to learn precision and clarity of thought.He wasn't very successful, but as my Latin dribbles away with years of inaction, I'm beginning to wonder whether maybe he had a point: a point made well by A.E. Housman (1989) in his inaugural lecture as professor of Latin
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".