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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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; both teacher heads agree on what is shown here.
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".