Towards a strategic understanding of global teams and their HR implications: an expert dialogue
Bibliographic record
Abstract
Drawing on initial insights emerging from a panel at the EIBA 2016 Conference in Vienna, here discussants and expert panelists engage in a follow-on conversation on the HRM implications of global teams for international organizations. First we set out how HRM can enable global teams and their constituent members to overcome the new and considerable challenges of global teams. These challenges span levels of analysis, time and space. Next we debate global teams as a strategic response to the dual pressures of global integration and local adaptation. We consider what HRM is needed for global teams to successfully resolve this dilemma, challenging practitioners to move beyond the ‘best practices’ and ‘alignment’ dichotomy. Lastly we look to the future to consider implications for research. We propose a rich research agenda focused on the complexities of the global team context.
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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 | 0.075 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.020 | 0.030 |
| Insufficient payload (model declined to judge) | 0.007 | 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".