Identifying and mitigating risks to the quality of open data in the post-truth era
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.
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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 | 0.147 | 0.534 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.018 | 0.034 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".