Bibliographic record
Abstract
A growing body of literature shows that social media plays a key role during crises and conflicts. In addition to traditional media, social media are used to mobilise people for a common cause and to communicate vital information. Very little is known about social media use during crises in the sub-Saharan African context. This article presents how Twitter is being used in the ongoing Cameroon Anglophone Crisis by several groups including the government, Anglophone activists, media organisations, and everyday citizens. Using critical theoretical perspectives to examine tweets from 1 September 2016 to 31 December 2018, this article identifies key themes. These include: placement of the crisis in a contested, historical context; debates about naming the crisis; key concepts; depiction of several forms of violence; and potential options for resolution. Social media is being used by the government, Anglophone activists, and non-affiliated people to sway public opinions on the crisis and solicit the attention of local, Diaspora, and broader international communities. Social media use has loosened the grip of governmental control of media messaging and expanded the public narratives available in Cameroon, yet at the time of the writing, this does not appear to have lessened the impact of the crisis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".