Uncertainty Orientation: A Theory of Self-Regulation Within and Across Cultures as Related to Cognition
Bibliographic record
Abstract
Erich Fromm once said “the quest for certainty blocks the search for meaning. Uncertainty is the very condition to impel man to unfold his powers.” For some, this quote is unmistakably true, impelling them to great discoveries of nature and the mind. For others, uncertainty is the very essence of confusion and ambiguity, offering nothing more than reason to retreat to more predictable and certain times. In this chapter, we explore the theory of uncertainty orientation as related to cognition and cognitive processes, including research that was conducted in Canada, Japan, and China. First, we discuss the characteristic uncertainty selfregulation styles that distinguish uncertainty-oriented individuals from certainty-oriented individuals. Next, we discuss the uncertainty orientation framework which integrates one’s uncertainty self-regulation style, the uncertainty present in the situation, and one’s characteristic motivations (e.g., achievement motivations) to predict performance outcomes in the related motivation domain. After discussing these basic tenants of our framework, we examine some of the cross-cultural research that has directly tested the predictions of the theory of uncertainty orientation. Concluding, we contrast our conceptualization of culture with how culture is commonly conceived in cross-cultural research.
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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".