Stay on the Wikipedia task: When task‐related disagreements slip into personal and procedural conflicts
Bibliographic record
Abstract
In Wikipedia, volunteers collaboratively author encyclopedic entries, and therefore managing conflict is a key factor in group success. Behavioral research describes 3 conflict types: task‐related, affective, and process. Affective and process conflicts have been consistently found to impede group performance; however, the effect of task conflict is inconsistent. We propose that these inconclusive results are due to underspecification of the task conflict construct, and focus on the transition phase where task‐related disagreements escalate into affective and process conflict. We define these transitional phases as distinct constructs—task‐affective and task‐process conflict—and develop a theoretical model that explains how the various task‐related conflict constructs, together with the composition of the wiki editor group, determine the quality of the collaboratively authored wiki article. Our empirical study of 96 Wikipedia articles involved multiple data‐collection methods, including analysis of Wikipedia system logs, manual content analysis of articles' discussion pages, and a comprehensive assessment of articles' quality using theDelphi method. Our results show that when group members' disagreements—originally task related—escalate into personal attacks or hinge on procedure, these disagreements impede group performance. Implications for research and practice are discussed.
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 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 | 0.012 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".