Indigenising Facebook language: Use of local languages in Facebook communication among a selected group of Kenyan internet users
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it