Edit Wars: Framing Contests, Argument Structure, and the Meaning of Inequality at Wikipedia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it