Rules of English Spelling and the Choice to Use t or s in Shun-Words: A Wink at Anglophone Cameroonian Students
Bibliographic record
Abstract
Although English spelling has been of significant interest to scholars since the 1950s, it has remained a major problem even to native speakers. One peculiar problem with it is the spelling variation of the noun formation suffix often represented in discourse as “shun,” mainly between -tion and -sion. Current textbooks of English grammar have generally not discussed rules of its spelling with either form, even though they do many others. However, following online resources, conflicting on how to spell it are in current debate, with two main schools of thought that each fall in line with one of two approaches that can be called the “word-based model” and the “base-word model.” In Achiri-Taboh (2018), I have shown that, in writing down words that end with “shun,” the base-word model is to be preferred, presenting argument for a synchronic rule following the base-word model with seven conditions to warrant the use of -sion as opposed to -tion, albeit with exceptions. Following current debates and a test of Anglophone Cameroonian students for their spelling preferences, the present study establishes the problem as global and compelling enough, especially for Non-Native users and learners of English, to warrant an address in grammar textbooks by means of available recourses like the recent base-word-based rule. The study also demonstrates that the prevalence of the problem actually stems from the lack of readily available spelling rules in grammar textbooks, and that there is a need for further research on spelling rules in English.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".