Face-threatening and Face-saving Speech Acts of Teachers: A Discourse Analysis of Classroom Interactions
Bibliographic record
Abstract
Politeness is fundamental to social order production and social interaction precondition. However, it is inevitable to encounter impoliteness in communication that could threaten a person's self-image. Hence, this study aims to explore the face-threatening acts of teachers in terms of positive and negative faces, as well as how they exhibit face-saving acts in classroom talks. This study employed a discourse analysis approach to investigate how teachers use language to threaten or save students' face needs during classroom interactions. The researchers collected the data from the video recordings taken by researchers from the twelve (12) research subjects' classroom discussions with their students at La Filipina National High School in Tagum City, Davao del Norte. The data was analyzed using the politeness theory of Brown and Levinson (1987) and revealed that the teachers' face-threatening acts in terms of positive face are an insult, disapproval, criticism, bringing bad news, threat, non-cooperation, and unleashed negative emotions. While in terms of the negative face, they employed reminders, accepting compliments, giving offers and suggestions. The analysis also showed that teachers exhibited face-saving acts (Bald- on record, positive politeness, negative politeness, and off record) directly and indirectly by telling jokes or giving hints. These study findings contribute to our understanding of the complex nature of face-to-face communication in the classroom and provide insights into how teachers threaten or manage face needs in their interactions with students.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".