MétaCan
Menu
Back to cohort

Concept development for kindergarten children through a health simulation

2003· article· en· W1532672918 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computer Assisted Learning · 2003
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
FundersOffice of International Science and Engineering
KeywordsContext (archaeology)Dental healthDental hygieneWearable computerProcess (computing)PsychologyComputer scienceMultimediaMathematics educationMedical educationDentistryMedicine

Abstract

fetched live from OpenAlex

According to many dental professionals, the decay process resulting from the accumulation of sugar on teeth is a very difficult concept for young children to learn. Playing the dental hygiene game with Thinking Tags not only brings context into the classroom, but also allows children to work with digital manipulatives that provide rich personal experiences and instant feedback. Instead of watching a demonstration of the accumulation of sugars on a computer screen, or being told about dental health, this simulation allows pre‐school children to experience improving or decaying dental health without any real adverse health effects. Small, wearable, microprocessor‐driven Tags were brought into the kindergarten classroom to simulate the decay process, providing information about sugars in foods and creating a discussion about teeth. Preliminary analyses suggest that this program was effective and enthusiastically received by this age group.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.050
GPT teacher head0.373
Teacher spread0.323 · 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