Spiritual Intelligence in Higher Education – adding a ‘third’ dimension
Bibliographic record
Abstract
We live in a knowledge society which requires us to acquire knowledge in order to be able to solve the problems ahead of us. We certainly learn a lot and we sometimes learn how to apply our knowledge to given problems. However, we still seem to fail at an astonishing rate, given the increasing amount of knowledge that has been collected. We seem to continuously create new problems while solving others. Complexity of life appears to go beyond the problem-solving knowledge we tend to apply. Increasing uncertainty calls for creating a meaningful future by wisely accepting our ignorance without losing confidence in what we do know and by acting accordingly. This article suggests a change of approach to higher education away from the focus on more expertise and knowledge to the ability to discover meaning in what we do and to jointly create a meaningful future. This will be based on a three-dimensional approach to knowledge and human intelligence. Approaches to knowledge- Concepts of Intelligence Introduced almost 100 years ago, the concept of “intelligence ” started decades of psychological research about cognitive skills and the development of
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".