The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse
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Computer Science > Computation and Language
arXiv:2606.20255 (cs)
[Submitted on 18 Jun 2026]
Title:The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse
Authors:Celestine Achi
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Abstract:We introduce the Meaning Intelligence Framework (MIF), a nine-dimension annotation and evaluation schema for Nigerian public discourse that separates surface sentiment from true communicative intent. Existing benchmarks for Nigerian languages, including NaijaSenti and AfriSenti, treat sentiment classification as a three-way polarity task (positive, negative, neutral). We argue that the dominant failure mode of AI systems on Nigerian discourse is not translation failure but context failure: the same utterance carries opposite pragmatic force depending on speaker, audience, and situation. The MIF operationalises this insight across nine scored dimensions: register, surface sentiment, true intent, irony, coded subtext, risk tier, annotator confidence, speaker emotion, and recommended communications action. We construct a 30-item calibration dataset spanning Standard English, Nigerian English, Nigerian Pidgin, and code-mixed registers, and evaluate a frontier language model (Gemini 2.5 Flash) under zero-shot and schema-informed prompting conditions. The headline finding is the Register Gap: zero-shot register classification accuracy is 33.3%, rising to 73.3% (+40 points) when the model receives the MIF schema in-context. The composite Meaning Intelligence Score increases by 5.4 points (73.2 to 78.6) under schema-informed prompting, with the largest practical gains in register identification, coded-subtext detection (+10 points), and strategic action recommendation (+10.3 points). We release the framework specification, annotation guidelines, and the 30-item public calibration set to support reproducibility, while retaining a private holdout corpus for contamination-protected evaluation.
| Comments: | Preprint. 12 pages, 2 tables. Supplementary materials: MIF Master Specification v2.0, Annotation Guidelines v1.0, and 30-item public calibration set with gold labels available from the author |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| MSC classes: | 68T50 |
| ACM classes: | I.2.7; H.3.1 |
| Cite as: | arXiv:2606.20255 [cs.CL] |
| (or arXiv:2606.20255v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.20255
arXiv-issued DOI via DataCite (pending registration)
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