A CRITIQUE OF THE ADEQUACY OF KENYA'S LEGAL FRAMEWORK FOR SOCIAL MEDIA REGULATION DURING ELECTIONS.
Bibliographic record
Abstract
Kenya experienced the negative impact of fake news during both the 2013 and 2017 general elections. As a consequence, the government has resorted to the use of draconian and antiquated laws such as the Penal Code and/or enacted some new laws aimed at regulating social media to tame the menace. The Constitution of Kenya on the other hand guarantees fundamental freedoms of expression, free media and access to information. This study argues that these fundamental freedoms face the danger of violation through the kind of legal regulatory mechanisms being deployed by the government. This study set out to examine the adequacy of the regulatory regime in place for the regulation of social media especially during elections, and whether it is suitable in upholding these fundamental freedoms. The study used qualitative research methodology, specifically doctrinal and comparative methods for data collection. For the theoretical framework, the study adopted the proportionality theory due to its potential in the resolution of conflicts between a right and a competing right or interest, which is at the core of this study. It was found that legal approaches to the regulation of fake news, especially criminalization, is counter-productive in a democracy. It was further established that other jurisdictions such as Singapore, Germany, Australia and Canada have preferred non-legal approaches such as self-regulation of social media which could be useful to Kenya. The study recommends a multi-pronged approach in order to effectively safeguard against the impacts of ill-speech. Specific recommendations are that Kenya should have better fact-checkers and authorities committed to responding to public interest; Kenya should adopt limited but necessary legislative support to help generate consensus with a much more effective regulatory mechanism; and that the Government should put in place a legal framework against problematic social media content and in any such strategy, the tech platforms are bound to play the biggest role.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".