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Record W3206549201

Nigerian Newsrooms Under COVID-19 Lockdown

2021· article· en· W3206549201 on OpenAlexaboutno aff
Fatima Iyabo Abubakre

Bibliographic record

VenueThe Political Economy of Communication · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperCoronavirus disease 2019 (COVID-19)Political scienceMainstreamPandemicQuarter (Canadian coin)EliteSocial mediaPublic relationsPopulationEconomic growthMedia studiesPoliticsSociologyGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

A global pandemic garners a great deal of media attention. The COVID-19 crisis is no exception. It was distinctive because more than a third of the world’s population had to undergo quarantine in the first quarter of 2020 which had far-reaching social effects. This study provides a historical overview of Nigerian newspapers and explores how the COVID-19 lockdown in Nigeria impacted upon the newsrooms of mainstream media organizations. It draws on interviews with heads of newsrooms and mid-management staff from six elite newspapers in the country. Findings reveal that although the pandemic was economically challenging for news organizations, they had no choice but to stay in business, providing information to the public during lockdown and the protests which followed. This took a toll on journalists whose work was impeded by mob and police actions and had broader implications for news production and the survival of news organizations.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.078
GPT teacher head0.310
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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