Disagreeing respectfully: embracing complexity facilitates civil discourse
Bibliographic record
Abstract
Polarization and incivility are on the rise, negatively affecting collegiality, workplace relationships, morale, and performance at work. The authors argue for the need for civil discourse in medicine and for embracing complexity as an essential component of that civil discourse, facilitating nuanced thinking, respectful dialogue, and greater understanding of other perspectives. This principle of embracing complexity is congruent with the attitude of physicians, who are trained to tolerate uncertainty and to hold and appreciate multiple perspectives in making diagnoses and choosing and proposing treatment plans. This understanding of civil discourse does not amount to moral relativism, whataboutism, or an embracing of both sides of an argument universally, nor does it serve as a cudgel to silence or to perpetuate hegemonic power. Instead, the principles of civil discourse clarify multiple aspects of the boundaries of professional conduct, outlining how physicians can engage in advocacy for patients and communities while maintaining collegial relationships and the perception that they will be safe providers for all patients. The rights of citizens in democracies, including to engage in peaceful protest and to say anything within the bounds of their country's laws governing free speech, do not extend unabbreviated into the lives of professionals, who are limited by the privileges afforded to them and by the responsibilities they have to their patients and colleagues. By embracing complexity and nuance over simplism and slogans, physician colleagues who disagree with one another can communicate respectfully, advocate professionally, and be safe and effective care providers to all patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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 teacher head, 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".