Bibliographic record
Abstract
Abstract Metalinguistic awareness is a cognitive process that allows a person to explicitly think about structural features of language such as phonological, morphological, and orthographic features and use this knowledge base to monitor and control his/her use of language. Metalinguistic awareness is strongly associated with monolingual children’s early literacy skills. The concept of metalinguistic awareness has also been used to explore the possibility of any paralleled mechanism that metalinguistic awareness operates in predicting bilingual children’s early literacy learning, especially between two languages that are orthographically distant such as Chinese and English. Research on Chinese-English bilingual children in both Chinese as a first language context (e.g., Mainland China, Hong Kong, and Taiwan) and Chinese as a heritage language context (e.g., Canada, United States, and the United Kingdom) confirms some cross-language facilitation of early literacy skills mediated by metalinguistic awareness in general, but overall research findings reveal a variance in terms of the directionality of transfer and aspects of transfer in predicting literacy skills in the two languages within the respective phonological, morphological, and orthographic awareness domain. Several linguistics-external factors such as individual children’s language proficiencies in the two languages and their exposure to formal language instruction mediate patterns of metalinguistic transfer in phonological, morphological, and orthographic awareness across the two different language contexts.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 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".