Perspectives on work, employment and management: Asia, comparisons and convergence
Bibliographic record
Abstract
This article outlines my long-term research and publication journey over what I consider to be important terrain, both academically and practically. The main contours of this revolve around the areas of work, employment and management in the context of Asia, comparisons and convergence. This retrospective has helped me to recall past publications—and with hindsight to focus more on their overall general implications and recommendations. These range from the macro down to the micro. These are that work and employment and its management remain important and a core parts of life, giving not only a sense of purpose, routine and meaning, but also independence and ability to connect and contribute to the lives of others and society. Within this, comparisons and cultural relativism are useful for contextualization in understanding not only change but also continuity around the area of work. This then requires broader and more nuanced views and perspectives with finer grain investigation and analysis using graduated concepts such as level, degree and speed in changes and continuities. Finally, the importance of not only managing change but also effective leadership and skills in the area runs through my traversing of the field.
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 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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".