EMPATHICA: a computer support system with visual representations for cognitive-affective mapping
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
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.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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