Bibliographic record
Abstract
When we learn new words we have to learn the form of these words, too.Learning the written form is referred to as orthographic learning.University students encounter disciplinespecific jargon when they are reading.To date, research has focused on orthographic learning in children.Accordingly, this thesis examined orthographic learning in a sample of university students and asked: (a) To what extent do university students demonstrate orthographic learning of novel words encountered exclusively through text?(b) What are the sources of individual differences in university students' orthographic learning?Specifically, this thesis considered the potential relation between orthographic learning and phonological shot-term memory, word reading ability, spelling and receptive vocabulary.The participants for this study were university students who were native speakers of English language.Because COVID-19 disrupted data collection, this thesis is based on a reduced sample size of 5 participants.These participants had an average accuracy of 63% for the orthographic form of the new words on the day one of testing and a mean of 74% correct three days later.Logistic regression was used to evaluate the relation between orthographic learning and individual differences.This allowed for the investigation of relations at the level of the item rather than participants.There were 32 items on the orthographic choice task, as such, across the five participants, there was a total of 160 items.Logistic regression across these 160 items revealed that students who were better spellers generally had higher accuracy on the orthographic learning task.Modelling also suggested that phonological short-term memory might be related to delayed retention of orthographic forms.Taken together, these results illustrate that university students learn the orthographic form of words encountered through texts.These results also help clarify some of the skills that university students might vii
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".