Bibliographic record
Abstract
Welcome to the first issue of the Journal of Work-Applied Management (JWAM) in 2024.The issue, released in the first half of 2024, reflects the emergence of artificial intelligence and how it is impacting the world of coaching (a prominent method of organisational change and development), well-being and applied strategizing in the dynamic contexts of volatile transport environments and entrepreneurship (also see the previous special issue on entrepreneurship).Once again, the editorial team, with the sterling support of the reviewers across the globe, offers an international issue with contributions from the UK, the US, the UAE, Canada, China, Ghana, India, Taiwan and Uganda.The first coaching article is "The library of Babel: assessing the powers of artificial intelligence in knowledge synthesis, learning and development and coaching" (Passmore and Tee, 2024), followed by "Hands up for homework: exploring inter-sessional activities in coaching" (Passmore et al., 2024).The juxtaposition of these articles highlights the need to consider the use of emerging digital technologies, which are increasingly effective, as well as one of the most popular interventions used within coaching (between sessions).Rendered through technology, coaching remains an exciting, evolving field of work-applied learning and management to monitor closely.The next six articles refer to well-being and healthcare in a broad sense.The first three are "A study on adaptive performance, work-related psychological health and demographics in Episcopal Church bishops" (Rowe et al., 2024), followed by "The influence of subordinates' proactive personality, supervisors' I-deals on subordinates' affective commitment and occupational well-being: mediating role of subordinates' I-deals" (Bhawna et al., 2024) and then "Employee empowerment and organizational commitment among employees of starrated hotels in Ghana: does perceived supervisor support matter?" (Kyei-Frimpong et al., 2024).These articles raise important insights about the linkages between personality, empowerment and commitment and how these can link to performance.These are important in applied research as they are a reminder of the differential outcomes of change efforts.The next three articles raise important meta well-being questions about industries or groups of people.They are "Healthcare employment landscape: comparing job markets for professionals in developed and developing countries" (Butt et al., 2024), "Intelligent careers and human resource management practices: qualitative insights from the public sector in a clientelistic culture" (Mouratidou et al., 2024) and "Paradoxical career strengths and successes of ADHD adults: an evolving narrative" (Crook and McDowall, 2024).Reading these articles together highlights the contemporary complex landscapes and environments that applied methods need to consider as they are adopted for positive change work.Within such contexts, however, it is crucial that we recognise the full spectrum of strengths and contributions within organisations (and society) and address exclusionthis does not benefit any organisational stakeholder.The final two articles are distinctive in terms of their industrial classification but complimentary in terms of how volatility and dynamism are understood and managed in organisations.First is "How do transport companies execute strategies in a volatile JWAM 16,1 2
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.060 | 0.048 |
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".