Bibliographic record
Abstract
In recent years, conflicts between doctors and patients in China have occurred from time to time. In the past, some scholars conducted research on the doctor-patient relationship, but there are few studies on doctors’ pragmatic identity construction. Therefore, guided by Chen Xinren’s pragmatic identity theory, using python as an analytical aid, this paper uses a combination of qualitative and quantitative analysis to conduct a study of doctor’s pragmatic identity construction based on a medical documentary The Doctors . The main focus of this study is not only the types of pragmatic identity constructed by doctors in the documentary, but also the emotional characteristics of these pragmatic identities. According to this research, the doctors in the documentary The Doctors mainly construct expert identity, peer identity, and stress bearer identity. The overall emotional characteristics of the constructed pragmatic identities are neutral, and positive emotions are greater than negative ones. This paper has certain research significance. For one thing, this study provides a new research perspective for doctors’ pragmatic identity construction, that is, to study the overall emotional characteristics of the constructed identities. For another, this study can help the public understand the pragmatic identity of doctors to a certain extent, and promote the harmonious relationship between doctors and patients.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".