FUNCTIONAL TEXT ANALYSIS OF OATH FOR ENGINEERS: ITS META-FUNCTIONS AND LINGUISTIC CHARACTERISTICS
Bibliographic record
Abstract
This paper aims at investigating the language structure of the oath for engineers in the light of Systemic Functional Linguistics and examining how the oath is textualized to accomplish its roles and objectives as the guiding principle which sets the ideals and obligation of professional engineers. Hence, the main objective of this study is to identify the pattern of the text�s structure by analyzing its metafunctions comprising the textual, interpersonal, and ideational functions. The main data used in this study is the text of oath for engineers as subscribed by the members of professional engineers in USA and Canada. The results of data analysis showed that the text has special features characterizing the genre of oath and its purpose as indicated by 1) the predominance of declarative clauses, 2) the equal usage of marked and unmarked themes representing the setting and reaction phases in the text, 3) the typical form of zig-zag pattern of the thematic organization, and 4) the dominant use of material processes indicating that the text construes the world more in terms of action with engineers at its center, 5) the validity of proposition of the oath for the present time when the engineers subscribe to the oath and to the actual situation for the future time as indicated by the tense of the clauses in the text. Thus, unfolding the discourse of oath in English for engineering class is beneficial to increase learning interactions in which reading is treated as the focus of the teaching as well as enhancing students� individual development as a part of character education.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".