Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of Nigerian Pidgin seems to have gained a wider currency since Nigeria’s independence in 1960. Among the educated and barely educated, the pidgin is used profusely in many spheres of life, especially in informal situations. In the mass media (television, radio, magazines and newspapers), schools, higher institutions of learning, government offices, etc., pidgin discourse abounds. In fact, despite the fact that it is not yet an official lingua franca in the country, it is a daily phenomenon in every day affair of an average Nigerian. The nature of Nigerian Pidgin, its easy mode of acquisition as well as the multilingual background of Nigerians may have been responsible for its present status and functions. In the light of the above, I am interested in how meaning is assigned to a piece of pidgin discourse, especially an advert in Nigerian Pidgin. Thus the goal of this paper is to establish how pidgin adverts communicate the intended meaning of their advertisers and how the audiences perceive them through an application of “Presupposition” and “Implicature” as conceptual or theoretical tools. These tools provide illuminating pragmatic insights and perspectives on Nigerian Pidgin media adverts.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it