Derivational Suffixes Analysis Found in “Every Summer After” Novel by Carley Fortune
Bibliographic record
Abstract
This study specifically concentrated on conducting an in-depth analysis of the various types of derivational suffixes. Derivational affixes were one of the most interesting topics to analyze, as they influence the formation of words from simple to complex ones. The data used in this analysis was sourced from the novel "Every Summer After" by the esteemed author Carley Fortune. Carley Fortune is a #1 Canadian national bestseller and has been honored with an award for her exceptional work as a journalist and author. This data was meticulously examined through the application of a qualitative analytical method. The research findings have been thoroughly explored using both formal and informal explanations. The analysis was based on Plag's theory (2003) and supported theory up by Carstairs-Mccarthy's theory (2002). This comprehensive approach helped in understand the findings in depth. This comprehensive linguistic analysis of the novel meticulously identified four distinct types of derivational suffixes: nominal, verbal, adjectival, and adverbial. The results revealed a notable prevalence of nominal suffixes, a total of 249 instances, and a smaller number of verbal suffixes, amounting to just 3 instances. Furthermore, this study found 96 instances of nominal suffixes, 86 instances of adverbial suffixes, and 64 instances of adjectival suffixes in the text. This thorough analysis provides a detailed examination of the intricate language intricacies found within Fortune's novel. Furthermore, it enhances the overall understanding of how derivational morphology is utilized in contemporary literature.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".