Crowdsourcing techniques for augmenting traditional accessibility maps with transitory obstacle information
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
One of the most scrutinized contemporary techniques for geospatial data collection and production is crowdsourcing. This inverts the traditional top-down geospatial data production and distribution methods by emphasizing on the participation of the end user or community. The technique has been shown to be particularly useful in the domain of accessibility mapping, where it can augment traditional mapping methods and systems by providing information about transitory obstacles in the built environment. This research paper presents details of techniques and applications of crowdsourcing and related methods for improving the presence of transitory obstacles in accessibility mapping systems. The obstacles are very difficult to incorporate with any other traditional mapping workflow, since they typically appear in an unplanned manner and disappear just as quickly. Nevertheless, these obstacles present a major impediment to navigating an unfamiliar environment. Fortunately, these obstacles can be reported, defined, and captured through a variety of crowdsourcing techniques, including gazetteer-based geoparsing and active social media harvesting, and then referenced in a crowdsourced mapping system. These techniques are presented, along with context from research in tactile cartography and geo-enabled accessibility systems.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.025 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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