Bibliographic record
Abstract
Text analysis is a way of describing language functions. A text is defi ned as any passage, spoken or written, that forms a whole. According to Halliday, every text has a texture, “A text derives this texture from the fact that it functions as a unity with respect to its environment.” Cohesive elements are important linguistic features that pitch in the textual unity. The aim of this paper is to use linguistic tools that are useful in analyzing and understanding any written text. The principles of referencing, substitution, ellipsis, conjunction, and lexical cohesion stated by Halliday and Hasan (1976) were applied on the selected short story to reveal the significance of the cohesive elements that are present in the text which provide semantic links among the words, phrases and sentences for the interpretation of meanings that exists within the text thus furnishing the texture of the text and transforming it into a piece of discourse. Understanding how cohesion functions within the text to create semantic links could be beneficial for students of English as a second or foreign language to help “decode” meaning. Key words : Text; Texture; Cohesive elements; Semantic links; Meanings Resume L’analyse du texte est une facon de decrire les fonctions du langage. Un texte est defini comme tout passage, parlee ou ecrite, qui forme un tout. Selon Halliday, chaque texte a une texture, “Un texte tire cette texture du fait qu’il fonctionne comme une unite par rapport a son environnement.” Elements cohesifs sont importantes caracteristiques linguistiques que la hauteur de l’unite textuelle. Le but de cet article est d’utiliser les outils linguistiques qui sont utiles pour analyser et comprendre un texte ecrit. Les principes de referencement, la substitution, l’ellipse, la conjonction, et la cohesion lexicale a declare par Halliday et Hasan (1976) ont ete appliquees sur l’histoire choisie courte pour reveler l’importance des elements de cohesion qui sont presents dans le texte qui fournissent des liens semantiques entre les mots, expressions et des phrases pour l’interpretation de la signifi cation qui existe dans le texte fournissant ainsi la texture du texte et en le transformant en un morceau du discours. Comprendre les fonctions de cohesion au sein de la facon dont le texte pour creer des liens semantiques pourrait etre benefi que pour les etudiants de l’anglais comme langue seconde ou etrangere pour aider a «decoder» sens. Mots cles : Texte; Texture; Elements cohesifs; Liens semantiques; Signifi cations
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".