Symbolic Solidarity or Virtue Signaling? A Critical Discourse Analysis of the Public Statements Released by Academic Medical Organizations in the Wake of the Killing of George Floyd
Bibliographic record
Abstract
PURPOSE: Many academic medical organizations issued statements in response to demand for collective action against racial injustices and police brutality following the murder of George Floyd in May 2020. These statements may offer insight into how medical schools and national organizations were reflecting on and responding to these incidents. The authors sought to empirically examine the initial statements published by academic medical organizations in response to societal concerns about systemic, anti-Black racism. METHOD: The authors searched for initial public statements released by a sample of academic medical organizations in Canada and the United States between May 25 and August 31, 2020. They assembled an archive with a purposive sample of 45 statements, including those issued by 35 medical schools and 10 national organizations. They analyzed the statements using Fairclough's 3-dimensional framework for critical discourse analysis (descriptive, interpretive, explanatory), which is a qualitative approach to systematically analyzing language and how it reflects and shapes social practice. RESULTS: Many statements used formal and analytical language and reflected hierarchical thinking and power differentials between statement producers and consumers. The authors identified several tensions in the statements between explicit messaging and implied ideologies (e.g., self-education vs action to address racism), and they found a lack of critical reflection and commitment to institutional accountability to address anti-Black racism in academic medicine. The authors also found that many statements minimized discussions of racism and de-emphasized anti-Black racism as well as portrayed anti-Black racism as outside the institution and institutional accountability. CONCLUSIONS: This research offers insight into how 45 academic medical organizations initially responded following the murder of George Floyd. Many of these statements included self-exculpatory and nonracist discursive strategies. While these statements may have been well intentioned, organizations must move beyond words to transformative action to abolish institutional racism in academic medicine.
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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 | 0.033 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.016 | 0.033 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".