The metric matters: determining the extent of children's knowledge of morphological spelling regularities
Bibliographic record
Abstract
All developmental research needs to carefully consider how children's knowledge is measured. The study of children's knowledge of spelling conventions, or the ways in which the English orthography encodes the roots and affixes and the sounds in words, is no exception. This experiment examined the extent of 7- to 9-year-old children's knowledge of the role of root morphemes in spelling words across different contexts and with different units of assessment. Different writing contexts did not appear to affect children's performance; children were better able to spell the first components of two- than of one-morpheme words (e.g. only free in freely and freeze), both when writing whole words and their first sections (e.g. completing__ or __ly for freely). A second analysis revealed that the unit of coding can influence conclusions. Children demonstrated similar abilities across ages 7 to 9 when only the first segments of words were coded; in contrast, there was evidence of age-related differences when whole word spelling accuracy was assessed. In combination, these results suggest that children's knowledge of the principle of root consistency is remarkably robust to changes in writing context, but that coding is key when drawing conclusions. These findings remind us that the metric matters in studies of spelling, as in other domains, and they offer a manner to reconcile previously conflicting data on spelling development.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".