The Usage of Spatial Prepositions in the Headlines of Major Nigerian Newspapers
Bibliographic record
Abstract
English language in Nigeria has the status as an official language that is used in local and international correspondences. One aspect of English grammar that is very hard for second language users including Nigerians to master is prepositions. Not only that English prepositions difficult, they are also the most frequently used items in newspapers that play an important role to signal political and cultural discourses. This study aims to describe the usage and communicative functions of spatial prepositions “in, on and at” in the headlines of four major Nigerian newspapers. Primarily, to achieve that the present study adopted Halliday’s Systemic Functional Linguistics. Data were collected through the analyses of 21 headlines from four selected major Nigerian newspapers. The findings revealed that Vanguard, a major Nigerian newspaper, has the highest percentage of the usage of target prepositions (26.86%), seconded by Punch (24.92%), followed by the Sun (24.27%), and lastly the lowest percentage, The Nation (23.95%). Also, it was revealed from the study that newspaper editors preferred to use preposition “at” (indicating specific location) but replaced it with preposition “in” (indicating broader location). Moreover, despite the frequent usage of preposition “in” in replacement of preposition “at”, they also preferred the forward position that expresses uncertainty compared to mid-ward and backward positions. This study concludes that people can be united ideologically especially on issues that foster nationalism through the use of newspaper headlines since newspaper headline is a new form of discourse that may initiate, sustain, and shape the political and other national agenda.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".