The life cycle of contributors in collaborative online communities -the case of OpenStreetMap
Bibliographic record
Abstract
Over the last two decades, online communities have become ubiquitous, with millions of people accessing collaborative project websites every day. Among them, the OpenStreetMap project (OSM) has been very successful in collecting/offering volunteered geographic information (VGI). Very different behaviours are observed among OSM participants, which translate into large differences of lifespan, contribution levels (e.g. Nielsen’s 90–9-1 rule) and attitudes towards innovations (e.g. Diffusion of innovation theory or DoIT). So far, the literature has defined phases in the life cycle of contributors only based on the nature of their contributions (e.g. role of participants and edits characteristics). Our study identifies the different phases of their life cycle from a temporal perspective and assesses how these phases relate to the volume and the frequency of the contributions from participants. Survival analyses were performed using both a complementary cumulative distribution function and a Kaplan-Meier estimator to plot survival and hazard curves. The analyses were broken down according to Nielsen and DoIT contributors’ categories to highlight potential explanatory variables. This paper shows that two contribution processes combine with three major participation stages to form six phases in contributors’ life cycle. The volume of edits provided on each active day is driven by the two contribution processes, illustrating the evolution of contributors’ motivation over time. Since contributors’ lifespan is a universal metric, our results may also apply to other collaborative online communities.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".