A Corpus Investigation: The Similarities and Differences of cute, pretty and beautiful
Bibliographic record
Abstract
Since the introduction of computers in corpus linguistics, analyses of language has transformed into a more reliable guide to language than n ative speaker intuition. When disagreements between speakers ’ intuition arise over the meaning and usage of words, an analysis of corpora can provide further insight on the characteristics of the words in question. In this study, a corpus analysis is conducted to investigate the similarities and differences in the use of cute, pretty and beautiful using the Bank of English (BoE). The investigation specifically looked at the frequency, collocation, semantic preference, semantic prosody and phraseology of the adjectives. The results show that similarities found between pretty and beautiful, according to these aspects, indicate that these two words may be the most synonymous pair of the three. However, the findings suggest that pretty and beautiful are far from being completely synonymous and do not have the same usage in all contexts. The analysis demonstrates that uncertainties a speaker may have regarding language use may be clarified by referring to corpora . Keywords: corpus; f requency ; collocation ; semantic preference ; semantic prosody
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".