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Record W4390268394 · doi:10.1080/1461670x.2023.2297784

Managing Difficult Relationships: The Case of Foreign Correspondents in Nigeria, State Officials, and Senior Editors in Overseas Media

2023· article· en· W4390268394 on OpenAlexaff
Levi Obijiofor, Marie M’Balla-Ndi

Bibliographic record

VenueJournalism Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsState (computer science)Political scienceMedia studiesSociologyLawComputer science

Abstract

fetched live from OpenAlex

This research systematically and empirically examines challenges that confront Nigerian foreign correspondents, including how foreign correspondents manage the demands of their job such as pressures from senior editors at their head offices, restricted access to information and state officials, and government officials interfering with objective news reporting.The study also looks at correspondents' accounts of their individual experiences and the strategies they deploy to circumnavigate the challenges they encounter in their professional practice.The merit of this research lies in its intent to fill existing gaps in the scholarship of foreign correspondence in Africa.Specifically, the study contributes to knowledge and understanding of foreign correspondents in Nigeria, the most populous country in Africa, including an understanding of the difficult environment of journalistic practices in a non-Western country.

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)0.004
metaresearch head score (Gemma)0.019
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 score0.039
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0390.011
Scholarly communication0.0140.007
Open science0.0020.008
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.070
GPT teacher head0.355
Teacher spread0.286 · 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
Published2023
Admission routes1
Has abstractyes

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