Exploring Transitivity in Speeches of President Joko Widodo Using UAM Corpus Tool
Bibliographic record
Abstract
This research investigates the transitivity system as a part of systemic functional linguistics theory together with the UAM Corpus Tools 3.2, developed by Donnel (2008) in the presidential speeches of the President of the Republic of Indonesia, Joko Widodo (hereafter JW). It focuses on analyzing processes, participants, and circumstances. The research is a descriptive qualitative study. The speech transcripts of President Joko Widodo in 2015 and 2018 are stored in a text file (.txt) with UTF-8 encoding. The findings of this research showed that material process types were found more than other process types in 2015 and 2018. This indicates that, by using material clauses, JW strongly desires to emphasize real work or action work in his speech. In terms of the participants, Actor and Goal were the most dominant in 2015 and 2018. In terms of Circumstance, Location, Cause, and Manner were the most dominant in 2015 while in 2018, Cause, Manner, and Location were the most dominant. Location is again one of the most dominant circumstance features in the text. This can be considered as consistency in JW’s speeches. On the other hand, the fact that JW utilizes the same elements from the speech from three years prior means that this might not be regarded as a breakthrough.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".