Bibliographic record
Abstract
This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Communication. Please check back later for the full article. Facework represents an important mediation of the intersection between an individual’s private self-conception and the individual’s need to cooperate—or not—in a society, especially at the interpersonal and organizational levels of communication. More clearly, facework builds on the notion of a metaphorical ‘face’, which represents how an individual is viewed—that is, respectfully or not—by others in an interaction. Facework is, then, in its basic form, the interpersonal skills or strategies (i.e., work) needed to maintain or elevate, and in some cases, hinder, others’ perception of an individual’s right to deserve respect. Culture mediates this interaction even further by dictating whose face an individual should be most concerned (i.e., face-concern) with during an interactional exchange. For example, individualistic cultures (e.g., United States, Canada, Germany) prioritize that individuals generally should be most concerned with protecting their own sense of respect (i.e., self-face) while interacting or in conflict, while collectivistic cultures (e.g., China, South Korea, Japan) prioritize the focus on maintaining the other individual’s (i.e., other-face) sense of dignity and respect in an interaction. Yet, individuals in either individualistic or collectivistic cultures may also choose to try to enact concern with both themselves and others in an interaction (i.e., mutual-face). Other iterations of facework strategies and/or concerns—all at least partially mediated by cultural values and social norms—have emerged, including: face-negotiating, face-constituting, face-compensating, face-honoring, face-saving, face-threatening, face-building, face-protecting, face-depreciating, face-giving, face-restoring, and face-neutral. Notions of face and facework has also given rise to several face-oriented communication theories such as Face-Negotiation Theory (FNT), which aims to examine and predict, generally, how individuals in various cultures might negotiate and manage conflict(s) and conflict styles. Original understandings of face are primarily grounded in Erving Goffman’s sociological work on facework and Penelope Brown and Stephen Levinson’s Politeness Theory, works that have been used to examine and compare communication practices in multiple intercultural and cross-cultural contexts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".