Responsiveness in context: Unpacking the causal model of the wisdom-responsiveness link
Bibliographic record
Abstract
By exploring the nuances of responsive phenomenology, Streib extends the science of wisdom beyond person-centric phenomena to include the social, interpersonal, and intersubjective dimensions. We applaud Streib’s efforts to enrich wisdom models and highlight several areas requiring further clarity, particularly regarding the causal relationship between responsive phenomenology and wisdom, and the role of broader cultural-historical factors for understanding the wisdom of responsiveness. Our commentary highlights the need for greater conceptual precision to differentiate responsiveness from related constructs in social psychology and calls for future research to delineate when responsiveness contributes to wisdom in varied contexts. Through this critical examination, we aim to advance the science of wisdom by emphasizing the significance of responsiveness within a comprehensive social-ecological framework, thereby fostering a deeper understanding of interpersonal and intergroup relations.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".