Some ‘Terrestrial’ and ‘Celestial’ Issues Encountered in Dowsing ‘Old World’ Historical Sites
Bibliographic record
Abstract
In the following text I’ve pulled together some observations on the effects of celestial and ground energies I have run into while dowsing in Italy. But this ‘Old World’ perspective comes from a Canadian who has been resident in Italy for almost 40 years. I discuss dowsing the influences of ground and sky energy, following a hypothesis often used by those sensitive to paranormal phenomena. That is that potentially, humans have access to much more information about their environment than is available to the conscious mind, and that dowsing without equipment is also an option. In fact, it is necessary to go outside the usual boundaries of dowsing to make some important points. For example, Figure 1 suggested by Long (1948), should be borne in mind as my guiding hypothesis: It is worth asking the subconscious mind ‘his/her’ opinion on events that are affecting the subjective or conscious mind. In my case, having spent the recent pandemic alone, and since I became a widower last year, I’ve come to appreciate the viewpoint of my subconscious. No doubt some would describe this as a mental aberration but I now have ‘conversations’ with my subconscious, whom I address as ‘Frederick’. His answers to my questions, when they come, often later, appear as spontaneous thoughts or in dreams, and have proven their worth in practice.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".