Bibliographic record
Abstract
The rising use of Artificial Intelligence (AI), Metaverse and Blockchain technologies has influenced capabilities in a wide variety of industries. These have now started to provide affordances for automating provision of insight and empowerment in coaching business leaders and staff. This paper discusses the risk, that as such technologies become smarter, human beings may abdicate their thinking capabilities, especially related to creative problem solving, to machine intelligence. The author calls for prevention of cognitive decline in human beings as machines get smarter. A route to this can be mapped through use of Industry 5.0, Fifth Industrial Revolution (5IR) approaches, that focus on a harmonisation of human and machine intelligence, ensuring human beings can make machines smarter and that machine intelligence can in turn make human consciousness advance. Practitioner Coaches, to future proof their practice, must ensure they embody 5IR mindsets, techniques and technologies. The coaching discipline that is fit for practice in 5IR environments and contexts is what the author has called “Coaching 5.0.” This paper looks at Coaching 5.0 components and how they can be adopted by coaches to ensure the future is sustainable, insightful and empowering one for their practice and for their clients.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".