Comparing policies for open data from publicly accessible international sources
Bibliographic record
Abstract
The Continuous Analysis of Many Cameras (CAM2) project is a research project at Purdue University for Big Data and visual analytics. CAM2 collects over 60,000 publicly accessible video feeds from many regions around the world. These data come from 10 national and international sources, including New York City, the city of Honk Kong, Colorado, New South Wales, Ontario, and the National Park Service. These video feeds were originally collected for improving the scalability of image processing algorithms and are now becoming of interest to ecologists, city planners, and environmentalists. With CAM2's ability to acquire millions of images or many hours of videos per day, collecting this large quantities of data raises questions about data management. The data sources all have heterogeneous policies for data use. Separate agreements had to be negotiated between each source and the data collector. In this paper, we propose to compare data use policies that are attached to the video streams and study their implications for open access. One restriction is that some sources limit the longevity of the data. As the value of this data becomes realized over the long term, issues of storage capacity and cost of stewardship arise.
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 | 0.215 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".