Linguistic Role in the Promotion of Development Discourse in the Six Self Help Groups in the Meru Speech Community
Bibliographic record
Abstract
This article examines linguistic role in the promotion of development discourse. Data is drawn from six self-help groups in Meru namely: Firstly, youth. The UN, defines ' youth ', as those persons between the ages of 15 and 24 years. In Africa youth refers to persons aged between 15 years and 35 years and were the ones studied in this study. Secondly, middle age. Middle age is the period of age beyond young adulthood but before the onset of old age . This study defined middle age as years between 36 years and 55 years. Finally, elders or old age . Most Britons define old age as starting at 59 years. The present study defined old age as years between 55 years and above. The respondents were selected using the judgmental sampling procedure. Langer’s social ground work theory studied the analysis of data in the study. The findings of the study were elicited using tape recorded interviews on some selected development topics that helped to illustrate the linguistic role in the promotion of the development discourse. The data for the study was represented both qualitatively and quantitatively. The findings of the study show the role of linguistic in promotion of development discourse. The educated respondents scored the highest percentages on development discourse topics whose original language of communicating them is English while semi-educated respondents scored highest scores on topics that required knowledge on the involvement of the respondents on the cultural life of community which is communicated from generations to generations orally in first language. The paper gives insight on how the linguistic codes spoken by an individual promotes development discourse and the practical method of the application of social ground work theory in development discourse study. Keywords : development discourse, linguistic codes, linguistic role, self-help groups, social ground theory
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".