How Professionals Cooperate through Conflicts: Networks and Social Face in the Workplace
Bibliographic record
Abstract
Conflicts are everyday sources of professional disagreement in the workplace. This article advances the study of professional conflicts by examining the symbolic interactionist processes through which professionals in South Korea cooperatively work through conflicts. Through ethnographic fieldwork conducted at a large hospital in Seoul in 2018, it is demonstrated that clinical professionals retain their poise and cooperate their way through conflicts by adhering to predetermined script-like ‘lines’ of action that mandate the protection of a triadic conception of social face: their own social face, that of their colleagues, and that of their hospital. Locked in disagreement over the risk profile of procedures for clients, embattled clinicians and nurses reroute conversations about conflicts to stress a shared identity in a bid to prevent humiliation, maintain network reciprocity, and preserve social face – of their dissenting counterparts, themselves, and their hospital. Professionals exercise a discerning level of heterogeneity in their conflict avoidance to maintain harmonious relationships, foster a personal brand of trust with clientele, and ultimately safeguard professional unity in the hospital.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".