UFOs, ETs, and Alien Abductions: A Scientist Looks at the Evidence by Don Donderi
Bibliographic record
Abstract
Many books about UFOs appear each year, yet few of these books are worth reading; UFOs, ETs, and Alien Abductions is one of those few. The author, Don Donderi, holds a doctorate in psychology and spent most of his career at McGill University in Montreal as a professor, dean, and researcher. His specialties are human visual perception and memory, with several books and more than one hundred research papers and technical reports to his credit. He began to read about UFOs when he was ten years old. The interest has stayed with him throughout his life and motivated him to investigate several sightings as the opportunities arose. In 1968 he participated in a review of occupant cases as a consultant for the National Investigations Committee on Aerial Phenomena (NICAP), then the leading U.S. civilian UFO investigations organization. In the 1990s, when abductions dominated ufology, he consulted on how to interpret the results of a Roper Poll designed to uncover the prevalence of abduction-like experiences in the general public, and participated in major meetings such as the 1992 Abduction Study Conference held at MIT. He further lent his psychological expertise to a personality test for separating simulated abduction claims from honest experiential reports, and to an experiment that compared symbols reported by abductees with symbols imagined by non-abductees.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".