Towards Quantifying Behaviour in Social Crowdsourcing Communities
Bibliographic record
Abstract
We analyze crowdsourcing communities by creating a detailed process for quantifying individual behaviour in online environments. The key feature of our communities is their social interactions so we call these social crowdsourcing communities (SCC). First, we derive factors based on actions captured about textual contributions. We interpret and name these factors. Then we demonstrate their utility in predicting the quality of team contributions. We capture the actions of members using measurable variables and perform factor analysis on these to produce factors of behaviour in SCCs (i.e. dimensions of behaviour). We derive factor scores for each member. An abstract notion of teams is used that is based on the social interactions. Team scores are then determined by the aggregation of the individual factor scores. The relationship between the team-level factor scores and the quality of contributions made by each team are then used as a proxy for team effectiveness. We found that member behaviour has three dimensions/factors: Impact, Activity, Policing/Rowdiness and there is a linear relationship between a team's contribution quality and their Impact scores. We also found a moderate negative linear relationship between the smallest Activity scores in each team with the quality of their individual contributions. This shows that teams that produce higher quality contributions tend to have higher total and maximum Impact score with lower levels of Activity. Thus, we demonstrate that properly aggregated behavioural factors can predict the quality of team-level contributions.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".