Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the idea of expatriate adjustment through naturally occurring data. Specifically, through an investigation of three e‐mails sent to the author by a friend, Doug, the paper explores the notion that adjustment is a fluid concept and that through qualitative research methods it is possible to appreciate the expatriate experience in the context of an expatriate's “whole life” of experiences. This is in contrast to positivist approaches to the study of adjustment which offer limited snapshots of adjustment at particular moments in time. Design/methodology/approach The paper investigates three e‐mails sent by Doug to the author. The e‐mails constitute a form of naturally occurring data, and through forms of narrative analysis the e‐mails are able to be examined to throw light on the process of expatriate adjustment. Findings The paper highlights ways in which qualitative research methods generally, and specifically when used in relation to expatriates, enable a fuller understanding of the processes of “adjustment” that expatriates experience and its relationship to their life as a “work in progress”. This type of research approach and analysis complements the more positivist study of expatriates. In some aspects it supports research findings on adjustment, but it serves to humanize the independent expatriate and their experience. Research limitations/implications The research is a case study of only a single subject. The paper suggests the potential for using naturally occurring data in the study of expatriates and independent expatriates in particular. Practical implications Stories of the experiences of expatriation offer insightful and “real” access to the lived experience of the expatriate. In this sense, they can be much more powerful than other forms of cross‐cultural training. Originality/value The paper highlights the importance of naturally occurring data and the need to consider “whole lives” in the past and present, of research “participants”.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".