One Size Does Not Fit All: Global Perspectives on IT Worker Turnover
Bibliographic record
Abstract
Although the IT workforce has become increasingly global, much of the research on issues related to IT workers published in leading academic journals is conducted in the U.S. However, the majority of the world does not share the same context as the U.S. Despite that, comparative studies exploring country differences rarely demonstrate how and why these differences occur on a global scale. By relying on a dataset based on a survey of more than 10 000 IT workers in 37 countries, we employed the decision tree technique to build an accurate model of IT job turnover in the U.S. We then applied this model to 36 countries to test whether it is more accurate in countries that are similar to the U.S. in terms of their geographical proximity to the U.S. and the proximity of their cultural, political, and labor market contexts. The findings demonstrate that while the U.S. model of IT job turnover is not necessarily less accurate for countries that are geographically farther from the U.S., it is less applicable in the countries with cultural, political, and labor market conditions different from those of the U.S. Thus, global IT managers are recommended to interpret the U.S.-centric literature with caution.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".