MétaCan
Menu
Back to cohort
Record W2781489134

More Linguistics for 10-Year-Olds

2018· article· en· W2781489134 on OpenAlexaff
Helen Goodluck

Bibliographic record

VenueIndiana Magazine of History (Indiana University) · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsClass (philosophy)LinguisticsSyntaxPsychologyPhonologyMathematics educationPedagogyComputer sciencePhilosophyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.024
GPT teacher head0.259
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueIndiana Magazine of History (Indiana University)Same topicReading and Literacy DevelopmentFrench-language works237,207