The Central Pronouns in Nigeria’s 2015 Presidential Debate: A Grammatical Analysis
Bibliographic record
Abstract
This grammatical analysis of the central pronouns in Nigeria’s 2015 Presidential Debate aimed at determining their occurrence, semantic manifestations, typological and thematic distribution, and textual functions. Twenty-three central pronouns with a combined frequency of 2409 were identified and analysed using Quirk et al.’s (1985) framework. The result showed a 58% representation and a frequency of 94.6 in 1000 words, with the forms we, you, it, I, they, our emerging the most frequent. Personal pronouns were a hundred times more frequent than reflexive pronouns and fourteen times more recurring than possessives. The 1st person, 2nd person and 3rd person forms respectively represent approximately one-half, one-quarter, and one-third of total person contrast made; however, a dominance of plural over singular was seen and this was more pronounced with 2nd person. Whereas the ratio of masculine to feminine was 22:1, neuter gender was generally dominant. A dominance of subjective case over objective case was revealed while genitive case featured as determinatives only. Pronominal choices were governed by theme, structure of responses, and idiosyncrasy, as I was more concentrated under motivation for contesting than any other theme and under recognising and justifying the problem than specifying actions to be taken or making concluding marks. The multiple-authored texts used manifestly exposed the diversity of pronoun forms and their combinatory possibility, which was advantageous since the focus was not a given politician’s idiolect but the use of an aspect of language in politics, namely the central pronouns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".