Epistemological role of human reasoning in data-informed decision-making
Bibliographic record
Abstract
Visual analytics was introduced in 2004 as a “grand challenge” to build an interdisciplinary “science of analytical reasoning facilitated by interactive visual interfaces”. The goal of visual analytics was to develop ways of interactively visualizing data, information, and computational analysis methods that augment human expertise in analysis and decision-making. In this paper, we examine the role of human reasoning in data analysis and decision-making, focusing on issues of expertise and objectivity in interpreting data for purposes of decision-making. We do this by integrating the visual analytics perspective with Decision Intelligence, a cognitive framework that emphasizes the connection between computational data analyses, predictive models, actions that can be taken, and predicted outcomes of those actions. Because Decision Intelligence models factors of operational capabilities and stakeholder beliefs, it necessarily extends objective data analytics to include intuitive aspects of expert decision-making such as human judgment, values, and ethics. By combining these two perspectives we believe that researchers will be better able to generate actionable decisions that ideally effectively utilize human expertise, while eliminating bias. This paper aims to provide a framework of how Decision Intelligence leverages visual analytics tools and human reasoning to support the decision-making process.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".