Analisis Tindak Tutur Direktif dalam Review Produk Online oleh Influencer Fadil Jaidi
Bibliographic record
Abstract
This research aims to analyze the behavior and function of speech acts used by Fadil Jaidi in reviewing products online, which encourages the interlocutor to take action. This research is based on persuasive language, which has a big role in influencing consumers. The methods used in this study are methodological approach and theoretical approach. The data sources used in this study are fragments of speech discourse included in directive speech acts in several videos uploaded by Fadil Jaidi on digital platforms. The results show that there are nine forms of directive speech acts in several online product review videos by influencer Fadil Jaidi, namely (1) suggestive speech, (2) ordering speech, (3) commanding speech, (4) forcing speech, (5) convincing speech, (6) inviting speech, (7) urging speech, (8) questioning speech, (9) requesting speech. With this research, readers are expected to be able to understand the forms of directive speech acts as well as the intentions of the speech performed by Fadil Jaidi through online product review videos.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".