Bibliographic record
Abstract
This research tackles the pragmatic analysis of deixis in a religious text. It aims at: 1) Identifying and showing the occurrences of deixis in the religious text. 2) Finding out the most dominant type of deixis in the text. 3) Analyzing the reasons behind using these types of deixis and how they affect the audience who hear or read the speech. The source of data was taken from a religious lecture presented by Imam John Starling at Queens College in 22/10/2014 about imaan (faith) which is taken as a sample. The procedure followed in this research was reading and writing down the deictic expressions: person, place and time deixis. The findings showed that person deixis occurred for 202 times, place deixis for 11 times and time deixis for 6 times only, which indicates that the most dominant type is person deixis. After analyzing the three types of deixis in this text, the researcher has concluded that the reason behind the frequent use of person deixis could be due to the particularity of the religious texts which are centered on the Divine Entity, thus the speakers/writers always making a reference to God by using the third person pronoun ‘He’. In addition, this kind of texts is usually about guidance and advice, therefore, the pronoun ‘You’ also occurs frequently to address the audience directly and to draw their attention. And since the adviser (imam) wants to make his audience feel that he belongs to them and shares with them the same destiny, he used the pronouns ‘We’ and ‘Us’. In return, place and time deixis are very few in this text and occurred mostly during narrating some stories and setting some examples.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 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".