Spelling: Processes and strategies in print and computer formats.
Bibliographic record
Abstract
Research addressing the role of format on spellers\u2019 abilities to recognize and correct errors has neglected to incorporate the variables of error type and strategic engagement in their studies. Thus, the three experiments in this dissertation examined the abilities of various types of spellers (above average, average, below average, and second language) across print and computer formats. The experiments introduced the role of attention as a factor in error blindness (i.e., inability to detect mistakes) for spelling recognition tasks, and the role of working memory in the graded quality of mental representations for spelling production tasks (i.e., correction of misspellings). In each experiment, spellers were randomly assigned to one of four counterbalanced groups. Spellers were asked to detect and correct misspelling for two essays in print and computer formats, identifying the spelling strategies applied. The studies compared word knowledge levels to error detection and correction abilities; attentional and working memory processes accounted for the influence of the type of error and format on spelling performance. Findings demonstrated inherent processing differences between spelling recognition and production processes and the masking effects of the application of strategies on spelling accuracy. Effects of error type in terms of saliency and clarity were found as phonological errors were easier to detect and correct, but morphological errors were more prone to error blindness regardless of the format. Spellers\u2019 quality of mental representations remained equally accessible due to their grounding in orthography. Format alone did not have an effect on accuracy, but did have an effect on strategic engagement. Format evidenced higher cognitive demands in the computer format and when spellers switched work from computer to print. These changes were explained by the operations of a modulatory mechanism that inhibits the kind of information to be processed in the graphemic buffer. It is concluded that language processing models ... can account for these findings by including the function of lexical strategies in reading and spelling tasks.\--P. ii-iii.
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 | 0.002 | 0.012 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".