Molecular Memories by Robert G. Jahn and Brenda J. Dunne
Bibliographic record
Abstract
For more than a quarter century, there was a surprising and curious intellectual ferment in the basement of the School of Engineering and Applied Science at Princeton University. This was the PEAR Lab, short for Princeton Engineering Anomalies Research Laboratory, and though its works and contributions to the world of consciousness research are widely known, there is a back story that is worth telling, not only for its intrinsic interest but for the more subtle implications and encouragements it brings. In the interest of full disclosure, the review author was part of the PEAR family for some 22 years. The PEAR Lab was built on a collaborative foundation laid by Bob Jahn and Brenda Dunne, and grew quickly into a role as a leading research center that was a magnet for professionals interested in the nature and capacities of human consciousness, and for students exploring the range of intellectual possibilities. It also drew ordinary and not so ordinary people from the public, as well as government and industry. The attraction of unusual and sophisticated research was enhanced greatly by a warm and welcoming environment different from what most of us envision as a university laboratory. One might say the place was more PEAR than Lab, and yet it hewed without question to the canons of best practice in scientific terms. Quite a place, deserving of the documentation, descriptions, and anecdotes gathered in Molecular Memories by Robert G. Jahn and Brenda J. Dunne.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".