Demonstrating the potential of Picture Pile as a citizen science tool for SDG monitoring
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
The SDGs are a universal agenda to address the world’s most pressing societal, environmental and economic challenges. The supply of timely, relevant and reliable data is essential in guiding policies and decisions for successful implementation of the SDGs. Yet official statistics cannot provide all of the data needed to populate the SDG indicator framework. Citizen science offers a novel solution and an untapped opportunity to complement traditional sources of data, such as household surveys, for monitoring progress towards the SDGs, while at the same time mobilizing action and raising awareness for their achievement. This paper presents the potential offered by one specific citizen science tool, Picture Pile, to complement and enhance official statistics to monitor several SDGs and targets. Designed to be a generic and flexible tool, Picture Pile is a web-based and mobile application for ingesting imagery from satellites, orthophotos, unmanned aerial vehicles or geotagged photographs that can then be rapidly classified by volunteers. The results show that Picture Pile could contribute to the monitoring of fifteen SDG indicators under goals 1, 2, 11, 13, 14 and 15 based on the Picture Pile campaigns undertaken to date. Picture Pile could also be modified to support other SDGs and indicators in the areas of ecosystem health, eutrophication and built-up areas, among others. In order to leverage this particular tool for SDG monitoring, its potential must be showcased through the development of use cases in collaboration with governments, NSOs and relevant custodian agencies. Additionally, mutual trust needs to be built among key stakeholders to agree on common goals that would facilitate the use of Picture Pile or other citizen science tools and data for SDG monitoring and impact.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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