Children’s Acquisition of Literacy in Syllabic Scripts: Annotated Bibliography
Bibliographic record
Abstract
The Children’s Acquisition of Literacy in Syllabic Scripts project synthesizes existing knowledge about children’s acquisition of literacy in syllabic scripts, biliteracy in syllabic and alphabetic scripts, and the impact of learning exceptionalities on the acquisition of literacy in syllabic scripts. Although English-language sources on the topic are few, what research there is available suggests that whether children first learn an alphabetic or syllabic script before starting to learn the other type of script is not the most important factor in terms of their ultimate literacy and/or biliteracy. Instead, what is more significant is that children are exposed to a rich linguistic input in both of their languages, together with being provided with a supportive teaching environment, alongside clear and explicit teaching instruction. Search parameters comprised combinations of the following words/terms: acquisition, acquisition of literacy, alphabetic languages, alphabetic scripts, Arctic Quebec, Baffin, biliteracy, Cherokee, Cree, dysgraphia, dyslexia, education, Indigenous, Inuit, Inuktitut, Inuttitut, Inuttut, Keewatin, kivalliq, learning, learning exceptionalities, literacy, Nunavik, Nunavut, orthography, reading, reading deficits, reading development, reading difficulties, reading problems, school, script, syllabic, syllabic languages, syllabic scripts, writing, writing system. The resources in the annotated bibilography represent the a range of approaches to understanding children's literacy acquisition in a syllabic scripts.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".