The nature, incidence, impact and integration of spontaneous parapsychological experiences: an exploratory mixed methods research study
Bibliographic record
Abstract
Anecdotal reports of paranormal experiences abound. In addition to the numerous books, films and media articles, there is a growing body of personal narratives which is readily accessed through online websites, blogs and chatrooms. By comparison, there is a paucity of documented research on spontaneous parapsychological phenomena in the academic literature. The current exploratory study sought to redress this imbalance by addressing the research problem: what types of paranormal phenomena do people spontaneously encounter, and are there unifying themes µi the reports of these experiences? This research followed the Mixed Methods Research model. Qualitative and quantitative data were gathered via an online survey instrument, which was written in English. Over three thousand (N=3194) self-selecting respondents completed the questionnaire and in total, 59 countries were represented. The majority of the paranormal experients were from the United States of America (N=l979), Australia (N=485), United Kingdom (N=252), and Canada (N=228). More women (62%) than men participated in the survey, and while the dominant age group was the 18-35 year olds (45%), this was closely followed by the 36-55 year olds (43%). The survey gathered information on ten categories of paranormal experience, namely deja vu, apparitions, near-death episodes, out-of-body experiences, psychokinesis, premonitions, auras, mediumship, reincarnation, and telepathy. The survey gathered statistical data on the type, frequency, and age at onset of each type of experience. Respondents were also invited to reflect on the possible causes and the personal impact of their own parapsychological experiences.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".