Inferential risk in Nigerian public health communication: coded dataset and codebook

Description

This dataset tests the hypothesis that Nigerian public health institutions manage anticipated interpretive trouble prospectively rather than only reactively — extending Elder and Haugh's (2023) interactional concept of unwanted inference to written, non-sequential institutional communication, where audiences give no observable uptake for institutions to repair. The data was gathered by close, iterative coding of five public-facing texts from three Nigerian federal health agencies (a NACA HIV/AIDS FAQ, an NPHCDA COVID-19 vaccine FAQ, and NCDC advisories on Ebola virus disease, Human Metapneumovirus and Lassa fever), following a six-stage sequence: identifying each available inferable, its institutional status, the inferential risk attached to it, the management strategy used, the preferred inference established, and the behavioural action that preferred inference supports. The data shows that of 68 inferables coded across 28 inferential episodes, 28 were institutionally preferred, 12 institutionally neutral, and 28 institutionally unwanted — meaning institutions do not treat all available meanings as problematic, but selectively intervene on a specific subset. The 28 unwanted inferables cluster into six recurring risk types (delayed or unsafe action, 8 episodes; stigma/exclusion and distrust/refusal, 6 each; panic/alarm, 5; complacency, 4; distorted responsibility, 1), each with the specific episode IDs that instantiate it. The notable finding is that institutions typically manage two opposed risks at once within a single episode (for example, panic against complacency in outbreak advisories) using recurrent strategies — explicit denial, inferential narrowing, causal reconstruction, authority invocation and calibration — and that recommended actions (testing, vaccination, reporting) are consistently anchored to a preferred inference established immediately beforehand, supporting the paper's claim that behavioural alignment depends on prior interpretive alignment. The "Codebook" sheet gives the operational definition, inclusion/exclusion criteria and decision question for every category, so the status and risk labels in "Inferential Field" can be checked against the textual evidence they claim to be grounded in — status was assigned only where the source text itself displayed denial, correction, qualification or a comparable form of distancing, not on the coder's own judgement of accuracy or harm. Coding was conducted by the first author and reviewed by the second, with disagreements resolved through discussion. Researchers extending this framework to other institutional genres should treat the codebook, not the raw counts, as the reusable component.

Steps to reproduce

1. Retrieve the five source texts from the official websites of the three Nigerian federal health agencies: the NACA HIV/AIDS FAQ, the NPHCDA COVID-19 vaccine FAQ, and the NCDC advisories on Ebola virus disease, Human Metapneumovirus and Lassa fever. Full citation details for each text are given in the "Corpus Overview" sheet. 2. Read each text closely to identify inferential episodes: stretches of discourse in which the text makes an interpretation available and displays that interpretation as potentially inconsistent with the institution's communicative purpose, through denial, correction, qualification, contrast, rumour labelling, definitional distinction, causal explanation or another comparable form of distancing. Discard any candidate episode where the only evidence for "unwanted" status is analyst intuition rather than something the text itself displays. 3. For each retained episode, apply the six-stage coding sequence set out in the "Codebook" sheet, in order: (a) identify the inferable made available by the text or invoked context; (b) determine its status as institutionally preferred, neutral, or unwanted, citing the textual evidence for that status; (c) for unwanted inferables, identify the inferential risk — the anticipated communicative, social or behavioural consequence if the inferable were accepted; (d) identify the institutional inferential management strategy used to constrain, redirect or replace it; (e) identify the institutionally preferred inference the intervention establishes; (f) identify the behavioural alignment — the action, disposition or allocation of responsibility the preferred inference makes reasonable. 4. Record each coded inferable as its own row in the "Inferential Field" sheet, nested under its episode ID, using the column definitions in the "Codebook" sheet as the coding rules for each field. 5. Have a second coder independently review all coding decisions, category boundaries, and any disputed or ambiguous cases; resolve disagreements through discussion until an agreed classification is reached. 6. Refine category boundaries iteratively by comparing coding decisions across all five texts, until each category is applied consistently across the full corpus. 7. Aggregate the coded inferables into the three summary tables — "Inferable Status Summary," "Analytical Stage Synthesis," and "Risk Cluster by Episode" — by tallying inferable statuses, cross-corpus patterns, and inferential-risk clusters (with their supporting episode IDs) directly from the "Inferential Field" sheet.

Institutions

• Ambrose Alli University

  Edo State, Ekpoma
• University of Benin

  Edo State, Benin City

Categories

Licence