Bibliographic record
Abstract
scientist to write up a credible report for a journal. Almost inevitably, the sci-entist would be a professional and need a salary. The first two experiments, the fourth, and the seventh have long been famil-iar to investigators of paranormal phenomena, as Sheldrake well knows and shows by his references. Most investigators of such phenomena would agree with Sheldrake that an acceptance of the reality of paranormal phenomena by the majority of scientists would make an important difference. It would change the priorities and the funding among scientists, but perhaps not much else. After all, surveys show that the majority of persons, even among West-ern peoples, already believe in the reality of paranormal phenomena, although only about one quarter of scientists do. Further evidence is unlikely to make much difference to the person in the street. The problem for scientists work-ing in this area is that of obtaining evidence that will be persuasive to other scientists. Some would say the problem is that of persuading other scientists to examine without prejudice the existing evidence. Thus the claim that these experiments "could change the world " seems somewhat grand, even grandiose. Despite my reservations about the practicality of Sheldrake's proposals and his claim for their significance, I commend this book for its clear exposition of the fundamental principles of experimental science. Even if it does not enlist many amateurs to work in science themselves, it will surely increase respect for science among all laypersons who read it. That in itself will be a notable accomplishment. The book has some interesting illustrations, for example of pigeons and pi-geon lofts. It contains extensive references and has a fully adequate index.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.620 | 0.639 |
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; the direct Gemma label and the distilled Codex classifier 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".