Bibliographic record
Abstract
Research has demonstrated the facilitative effect of morphological word families on the development of orthographic representations and children's spelling accuracy.Specifically, related words that pronounce silent-letter endings increase the accuracy of children's attempts at spelling these particularly challenging French words.The current study intended to replicate and extend these findings in large-scale analyses assessing spelling accuracy by word for a large corpus of French words spelled by 40 children in Grades 1 through 5.Results supported the word family hypothesis in demonstrating the facilitative effect of morphological word families for all children.The derivative diversity hypothesis extended research on adult orthographic representations in finding that the diversity of derivatives was the stronger predictor of spelling across grades.Finally, the feminine form hypothesis was generally supported in that the feminine inflection was related to spelling only in Grade 1, however, this was only after controlling for the diversity of derivatives.iii Acknowledgments In the last year, I often felt frustrated and discouraged, eager for the end yet fearful of the changes that will ensue.Now that I am here I feel proud of my accomplishments, confident in my ability to face the challenges to come, and gratitude towards those who have helped me along the way.First and foremost, I thank Dr. Monique Sénéchal.Not only for your constant support and confidence in my ability to finish this project, but also for the multitude of opportunities you have provided over the years.Thank you for your patience and for helping me balance school with home.I thank my husband, David Taylor, for his support over our 10 years together; I would not be here without you.To my children, thank you for all your help at home and for your independence.I could not have done this without the constant support from my family, especially my parents and in-laws.A special thank you to Maxime Gingras, not only for your help with Silex and your linguistic expertise, but mostly for your dedication to perfection, unending kindness, and patience in helping me understand.I also want to thank the members of the CLLR lab that I have known over the years, your friendships have made this time enjoyable and I have always valued your help.Finally, thank you to my committee members for your time and the insightful feedback, and to all the Carleton faculty and staff who have helped me along the way.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".