EMPATHICA: a computer support system with visual representations for cognitive-affective mapping
Bibliographic record
Abstract
EMPATHICA is a computer program under development to facilitate cognitive-affective mapping using visual representations. A cognitive-affective map is a concept graph that includes information about the positive and negative emotional values of what is represented. Potential applications include conflict resolution, literary analysis, cross-cultural understanding, ethical assessment, authoring systems, and cognitive modeling. Cognitive-Affective Mapping Researchers in psychology, computer science, and political science have used the technique of cognitive maps (also known as conceptual graphs, concept maps, and mind maps) to represent the conceptual structures that people use to represent important aspects of the world (e.g. Axelrod 1976, Novak 1998, Sowa 1999). But such maps fail to indicate the values attached to concepts and other representations such as goals, and therefore are inadequate to capture the underlying psychology of conflicts and other important domains. They lack an appreciation of affect, which is the complex of emotions, moods, and motivations that are crucial in human thinking. (Note: there is also a quite different use of the term “cognitive map ” referring to mental representations of spatial knowledge.) A cognitive-affective map is a visual representation of the emotional values of a group of interconnected concepts. Such maps can be produced using any drawing program, but my colleagues and I are developing a computer program written in Java to further their production and application. It is called EMPATHICA, reflecting the hope that the program can be used to increase mutual understanding between people in conflict situations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.014 |
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".