Crowdsourcing techniques for augmenting traditional accessibility maps with transitory obstacle information
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.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".