Athari za Mabadiliko ya Maana katika Lugha ya Kiswahili: Mifano kutoka Matini za Kidini
Bibliographic record
Abstract
Makala hii inachanganua matumizi ya lugha katika mahubiri ya kidini. Azma kuu ni kuchunguza jinsi maana za maneno zinavyobadilika kiisimu zinapotumiwa kwenye miktadha ya mahubiri ya Kikristo. Data ya utafiti imetokana na unukuzi kimaandishi wa vipindi 5 vya mahubiri ya Kikristo yaliyorekodiwa kati ya Mei na Agosti, 2020 kutoka idhaa tatu za runinga nchini Kenya, yaani KBC, SAYARE na MBCI. Wahubiri 5 waliotumia Kiswahili tu kwenye vipindi vya idhaa hizi ndio walioteuliwa kwa usampulishaji kusudio. Data ya semi 64 zilizoteuliwa kwa usampulishaji huo zilichanganuliwa kwa mwongozo wa madhumuni ya utafiti pamoja na mihimili ya Nadharia ya Pragmatiki Leksika. Matokeo yanaonesha kwamba baadhi ya maneno yaliyotumiwa na wahubiri wa dini za Kikristo huwasilisha maana kipragmatiki. Maana za maneno hayo kimatumizi ni tofauti na maana msingi. Aidha, uchunguzi huu unaazimia kuendeleza mtazamo wa isimu kuhusu jinsi matumizi ya lugha katika muktadha wa sajili hii maalumu unavyoathiri mawasiliano.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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".