Contributors’ enrollment in collaborative online communities: the case of OpenStreetMap
Bibliographic record
Abstract
The number of people registering in an online community depends on two main factors: interest in, and awareness of, the project. Registering to a project does not, however, imply contributing to it, as lacking the knowledge and skills can be a barrier to participation. In order to identify the nature of events that might have facilitated or hindered enrollments in the OpenStreetMap (OSM) project over time, we analyzed the correlations between the number of new participants and the events that dotted its history. Four different metrics were defined to characterize participants’ behaviors: the daily number of registrations, the daily number of participants that made a first contribution, the delays between contributors’ registration and their first edits, and a daily contribution ratio built from the number of new contributors and the number of new registered members. Time series analyses were used to identify trends, and outstanding variations of the number of participants. An inventory of events that took place along the OSM project’s history was created and appreciable variations of the metrics have been linked to events that seemed to be meaningful. Although a correlation does not imply causality, many of the explanations these correlations suggest are supported by the results of other studies, either directly or indirectly. For instance, when considering the time participants spend as “lurker”, as well as on the nature of the contribution of early participants. In other cases, they suggest new explanations for the origin of the spam accounts that affect registration statistics, or the decline in the proportion of registered members who actually become contributors.
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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".