Deixis Used on Business Brochures Text: A Pragmatics Study
Bibliographic record
Abstract
Deixis is one of which takes some elements of its meaning from the situation, such as person, place, time, discourse and social. Deixis is an important of language study in which English as a foreign language. Deixis refers to the phenomenon where in understanding the meaning of certain words and phrases in an utterance requires contextual information. This is a descriptive qualitative research; data are brochures taken randomly from launching products, in March 2014. There are 32 brochures that were analyzed. The result of the analysis of the research is that there are 5 types of deixis used on business brochures text; 16.33% used Person Deixis, 5.71% used Location/spatial Deixis, 5.31% used Temporal Deixis, 63.27% used Discourse Deixis, and 9.39% used Social Deixis. Discourse Deixis is the most dominantly used in business brochures text. Discourse deixis, refers to a text deixis, which is the use of expressions within an utterance use in written language. It contains reason, description, background, and sophisticate technology explanation. The goal of preparing business brochures is to give clear description, detail of the product, the specimen, and the new technology. Writers draw a conclusion that if the brochures are for inexpensive product, person deixis is mostly used; on the contrary if the product is expensive they use discourse deixis with more explanation and description.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".