Indigenising Facebook language: Use of local languages in Facebook communication among a selected group of Kenyan internet users
Bibliographic record
Abstract
This paper interrogates how Facebook use in Kenya is being localised to serve everyone: Local people and the elite. With approximately three billion monthly active users as of the second quarter of 2023, Facebook is the most used online social network globally. In the second quarter of 2017, the platform surpassed two billion active users, a feat accomplished in just over 13 years. Facebook (FB) has permeated the lives of millions of people and the way they relate to one another and share information. This article examines how selected Kenyans are indigenising Facebook by using other local languages. The article recognises the utility of FB as a novel tool to examine and interpret linguistic features for a selected group of Kenyan FB users. The article uses Herring’s Computer-Mediated Discourse Analysis (CMDA) theoretical framework. The research design used was both qualitative and quantitative. A purposive sampling procedure was used to arrive at eight FB friends in the 22-35 age bracket. This is the age that was found to use FB most in Kenya. The findings showed that Kenyans localised Facebook use in Kiswahili, vernacular, and Sheng.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".