Identifying and mitigating risks to the quality of open data in the post-truth era
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
Big Data analysis often relies on open data, integrating it with large private data sets, using it as ground truth information, or providing it as part of the input to large simulations. Data can be released openly by governments to achieve various objectives: transparency, informing citizen engagement, or supporting private enterprise, to name a few. To the latter objective, Big Data analytics algorithms rely on high-quality, timely access to various data sources, including open data. Examples include retail analytics drawing on open demographic data and weather forecast systems drawing on open weather and climate data. In this paper, we describe the rise of post-truth in society, and the risks this poses to the quality, integrity, and authenticity of open data. We also discuss approaches to identifying, assessing, and mitigating these risks, and suggest future steps to manage this data quality concern.
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 | 0.039 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.012 | 0.012 |
| 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