Bibliographic record
Abstract
This paper is a short report on a linguistics class that I taught in College for Kids at the University of Wisconsin-Madison in July 1988. The children in the class were eight children who had just finished fifth grade; they were children who were identified as gifted. The class lasted for about two hours on three consecutive mornings. The class took place on campus and the children were accompanied by their teacher, Fred Bartling. The day after the course was over I made notes about what we had done. This report is based on my notes and my memory. I might say at the outset that I started off on the first day with the misguided idea that 10-year-olds could be given a watered-down version of the sort of linguistics class that goes down well with undergraduates (it might have helped if I had read Fabb (1985) before the class). My experience on the first day of trying to interest the children in the difference between particles and prepositions ('John looked up the road'; 'John looked up the number') made it clear that the facts had at least to be presented in a different way, and a lot of my time in the next day or so was spent trying to work out linguistic activities and games that 10-year-olds would enjoy. What follows is a list of things that seemed to work (and some that didn't), organized according to topic (syntax, morphology, phonetics and phonology, and language acquisition).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".