Bibliographic record
Abstract
There is a dearth of studies on mood and modality as a focus. This study examines the manifestation of mood and modality in texts. It identifies their pattern of occurrence, compares their frequency, and accounts for possible differences between their manifestation and reported norms. The data comprise 3 069 verbal groups. It was obtained by orthographically transcribing Christian Religious Knowledge, Geography, Physics, and Chemistry lessons recorded in schools in Lagos State, Nigeria and identifying all the verbal groups therein. The topics taught were respectively The Mission of the Church, The Drainage System, Electric Field, and Nitrogen. The scale-and-category version of the systemic grammatical model, complemented by simple percentages, served as analytical tool. Results show that mood represents 72 per cent of the data and 81 per cent of the finite verbal groups. Declarative mood, interrogative mood and imperative mood represent 55 and 62, 10 and 11, and 7.4 and 8.3 per cent respectively of the data and finite. Verbal groups marked for imperative mood occurred most frequently in segments of the Physics lesson involving strict computation. The non-polar interrogative mood dominated (73 per cent) its polar counterpart. Modality accounted for 13 per cent of the data and 14 per cent of finite. Root modality and Epistemic modality manifested at a ratio of 3:2 in favour of Root meaning. Only in Physics was Epistemic modality (63 per cent )higher than Root meaning. Will and can exceeded the reported 4.2 and 3.5 in 1 000 word-occurrence by 67 and 46 per cent respectively. PREDICTION was the most recurring specific modal meaning. The fact that every verbal group in predicator function selects from the system of mood partly explains mood’s dominance over modality in the texts analysed.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".