Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the cultural learning process (namely, the development, practice and enhancement of cultural intelligence (CQ)) of a successful entrepreneur – Harold Bixby, a Pan American Airways expatriate, as reflected in the memoir of his experiences in China during 1933–1938. Design/methodology/approach This study adopts a microhistory approach as a methodology for studying history and the past while ultimately requiring evaluations informed by the present. This paper first identifies the literature gap on CQ development and the need to study historical accounts of the past in assessing the CQ development process. This study then outlines the four key foci of microhistory as a heuristic for making sense of on-going and past accounts of selected phenomena. Findings This paper finds that specific personality traits (namely, openness to experience and self-efficacy), knowledge accumulation through deep cultural immersion (namely, extensive reading/study, visiting/observation and interacting/conversation), critical incident and metacognition all contributed to Bixby’s CQ development, which was a time-consuming process. Originality/value The study contributes to debates around cultural learning and historical organization studies by providing a rich, qualitative study of CQ assessment and CQ development through microhistory. This study highlights the importance of cognitive CQ and the function of extensive reading/studying in the process of knowledge accumulation. This paper draws attention to critical incidents as an underexplored way of learning tacit knowledge. Moreover, this study suggests metacognitive CQ can be enhanced through meditative and reflexive teaching and research practices. These findings have significant implications for cross-cultural training programs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".