A longitudinal investigation of self-initiated expatriate organizational socialization
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the process of self-initiated expatriate (SIE) organizational socialization. Design/methodology/approach To assess the socialization process, data were collected at three points in time. SIE English teachers were surveyed at three points in time. At Time 3, data from the principals of those teachers who completed surveys at Time 2 were also collected. Findings Organizational socialization tactics facilitate social integration and learning speed, which, in turn, are positively related to SIE adjustment. Moreover, SIEs who climbed the learning curve more quickly were only able to capitalize on their learning ability to promote performance when their calculative commitment was low. Originality/value First, in contrast with the majority of expatriate socialization studies that tend to focus on the proactive behaviors of expatriates, the authors examine the organizational socialization tactics of a local host organization. Second, they consider the role of calculative commitment, which is especially germane to the SIE context, on SIE performance. Third, this study contributes to the organizational socialization literature by recognizing that socialization is an on-going process that continues to influence employees even after they are no longer “newcomers.” Fourth, the authors assess adjustment directly rather than through proxy measures.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".