Two Years of Ups and Downs: Barack Obama's Patterns of Integrative Complexity, Motive Imagery, and Values
Bibliographic record
Abstract
President Obama's weekly radio addresses to the nation during his first two years in office were scored using thematic content analysis (TCA). TCA is a method for deriving quantitative data from qualitative materials through the use of detailed scoring manuals applied to oral or written texts by trained, reliable scorers. We scored the addresses for integrative complexity (IC), motive imagery (MI), and universal values. Obama's mean IC was second highest among recent presidents. His IC fluctuated in response to situational parameters, rising when he was negotiating and maneuvering his policies through Congress, falling when stress was high and a problem seemed amenable to a simple solution. His MI showed Achievement as his predominant motive. Achievement, Security, and Power were highest in his value hierarchy, which remained stable throughout the period; surprisingly, his ranking of Self‐Direction was much lower than a previously published pan‐cultural average. Last, we identified six clusters, time periods when his IC and Power imagery moved in opposite directions. The implications of this pattern for cooperative versus adversarial approaches in problem solving are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".