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Record W4400525583 · doi:10.4000/1208e

Impact(s) des technologies numériques en période de crise : cas du conflit à Mango, Togo

2017· article· fr· W4400525583 on OpenAlexaff
Namoin Yao

Bibliographic record

VenueCommunication technologies et développement · 2017
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsImpact
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

La ville de Mango située dans la région septentrionale du Togo a été le théâtre d’une crise dont les médias ont largement fait écho au mois de novembre 2015. Au cours de ces évènements, on a observé une grande mobilisation des populations riveraines et des natifs de cette localité résidant à Lomé. Cet article s’intéresse à la manière dont ils ont interagi avec ces différents outils (internet et téléphonie mobile), les finalités et l’impact de ces usages dans la résolution de cette crise. L’analyse de ces évènements à travers la couverture médiatique de la presse écrite et les différents outils ont révélé des déficits communicationnels au niveau de la conduite de ce projet. Elle a surtout permis d’inscrire les usages dans un contexte social caractérisé par des pesanteurs politiques déterminant la résolution de ce conflit.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.742
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0060.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.049
GPT teacher head0.350
Teacher spread0.300 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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