Slot-Filler and Taxonomic Organization: The Role of Contextual Experience and Maternal Education
Bibliographic record
Abstract
Previous studies on children’s semantic development suggest a shift from slot-filler to taxonomic organization at around eight years of age. However, these studies typically did not include children of early elementary-school ages (six- or seven-year-old); hence the possibility remains that the shift could have emerged earlier in development. The goal of the present study was to examine the age at which the taxonomic advantage in semantic organization occurs in a cross-sectional sample of children covering a wider age range and elucidate the factors related to the use of different semantic organizational strategies. Forty-six Mandarin-English bilinguals belonging to three age groups (five-, six-, and seven-year-old) were administered category generation task in both slot-filler (e.g., “Name all the zoo animals you can think of.”) and taxonomic conditions (e.g., “Name all the animals you can think of.”). The taxonomic advantage emerged as early as six years of age. Knowledge of specific slot-filler categories (farm vs. zoo animals) showed different age-related changes. Age and maternal education more consistently predicted performance in taxonomic than slot-filler condition. The slot-filler to taxonomic shift in semantic organization is exhibited across populations of distinct language background in early school-age years. Experience with specific categories and parental input both play important roles in the development of semantic organization.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".