“I can’t be neutral or centrist in a debate over my own humanity”: A Study of Disagreements Between Journalists and Editors, and What They Tell Us About Objectivity
Bibliographic record
Abstract
Journalistic objectivity has long been in flux. This paper examines cases in which we see journalists aiming to subvert norms, and managers pushing back, reprimanding the journalists and removing them from coverage or firing them. Understanding what’s happening at these edges of acceptable journalistic practice can offer clarity about the nature of change in the field. We find journalists arguing that objectivity works differently when reporting on minority groups—so much so that they suggest focusing instead on context and truth in these cases, while managers counter that objectivity is universal. We note that scholars offer alternatives—Ward’s “pragmatic objectivity,” which recommends taking the perspective of the community, and Durham’s “strong objectivity”, which suggests embodying the most marginalized groups in a discussion. This examination offers insight into how journalism is evolving, in particular in a moment of racial reckoning.
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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 | 0.112 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.031 | 0.100 |
| Scholarly communication | 0.035 | 0.030 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".