Edit Wars: Framing Contests, Argument Structure, and the Meaning of Inequality at Wikipedia
Bibliographic record
Abstract
Collaborative knowledge platforms like Wikipedia influence how individuals understand and interpret issues, yet how the meaning of issues is co-constructed on such platforms is poorly understood. Drawing from a longitudinal study of the discussion of inequality on Wikipedia’s “Capitalism” page, we uncover the process of collective meaning making around contentious issues on collaborative knowledge platforms. Incorporating insights from research on framing and argument structure, we demonstrate how new frames of inequality gain traction on the page. Through an analysis of the frontstage framing contests and backstage negotiations between opposing editors, we find that factors such as emotivity and the characteristics of the frame articulator that can support frame traction in other settings work against it at Wikipedia. Instead, we show how frame traction on these platforms is supported by external social movement activity that stimulates the creation of discursive resources that advance specific frames and the addition of subjective qualifiers that make claims advancing certain frames more palatable to opposing editors. We discuss the implications of these empirical discoveries for research on collaborative platforms and for wider scholarship on collective meaning making around contentious issues.
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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 | 0.015 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".