Value Differences: A Starting Point for Influence
Bibliographic record
Abstract
Cognition happens in the mind involving the ability to think, perceive, and reason. Cognitive Warfare targets our ability to think, make decisions, and ultimately act. Values are individual beliefs that motivate and guide actions. They also inherently affect our cognition. Narratives are a way of transmitting values that are directly dealt with by our cognition. And finally, the information environment is used to broadcast all forms of narratives. While there are inevitably kinetic wars raging, the larger battlefield is in our minds. The narratives that capture the most believers will dominate the worlds actions. With these connections made, we hypothesise that there are significant common values that are shared between democratic countries and specifically between NATO nations that lead to a set of common narratives. In contrast, autocratic nations' narratives present differing values (or at least of a different flavour) that are designed to produce a form of cognitive dissonance and affect behaviour. This paper examines the first part of the hypothesis by surveying democratic values. We will examine three case studies to start unravelling the nature of values and their flavours. First, we record the data collection and results from the appreciative inquiry exercise conducted during the IST-195 symposium. Second, we look at far-right extremism through the lens of values; the case of French fachosphere. Finally, we examine the values of countries rated as "top countries to live"(Staff, 2023b). The Schwartz model is used to categorise, map, and compare the values expressed by each use case. This paper will set the stage for our ability to discover information narratives that support or contradict NATO values and their specific flavours. Such a capacity will provide focus for cognitive warfare, information operations, strategic communications, as well as allow for measures of effectiveness in the information environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.010 |
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; both teacher heads agree on what is shown here.
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".