A Contrastive Functional Analysis of Reference as a Cohesive Device in the English Language and Ika language
Bibliographic record
Abstract
Many definitions of language underline the fact that language is mainly a tool for communication. As a tool for communication, a language has a structure which makes it organized and understandable in relaying meanings to the users. One of such patterns of a language is found in the cohesive ties that run through the language linking what is being said to what has been said and what is to be said. This study compares reference a cohesive [Z1] tie in the English language and the Ika language. The study uses the Lexical and Grammatical cohesion Model proposed by Halliday and Hassan, (1976 and Chesterman’s Contrastive Functional Analysis (CFA) (1998) as the theoretical frameworks. The study establishes the similarities and differences in the functions and layout of referencing cohesive elements in the English language and Ika [Z2] language. The study is done with the aim of promoting the translation of text [Z3] s to and from both languages and improving the learning of English as a second language. In order to get the right data for the study, we devised the Ika English Cohesive Contrastive Template (IECCT) and applied a random sampling technique. The study presents findings and contributions to knowledge. [Z1] a cohesive [Z2] Ika language [Z3] texts
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".