ASSESSING VOLUNTEERED GEOGRAPHIC INFORMATION (VGI) QUALITY BASED ON CONTRIBUTORS' MAPPING BEHAVIOURS
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
Abstract. VGI changed the mapping landscape by allowing people that are not professional cartographers to contribute to large mapping projects, resulting at the same time in concerns about the quality of the data produced. While a number of early VGI studies used conventional methods to assess data quality, such approaches are not always well adapted to VGI. Since VGI is a user-generated content, we posit that features and places mapped by contributors largely reflect contributors’ personal interests. This paper proposes studying contributors’ mapping processes to understand the characteristics and quality of the data produced. We argue that contributors’ behaviour when mapping reflects contributors’ motivation and individual preferences in selecting mapped features and delineating mapped areas. Such knowledge of contributors’ behaviour could allow for the derivation of information about the quality of VGI datasets. This approach was tested using a sample area from OpenStreetMap, leading to a better understanding of data completeness for contributor’s preferred features.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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