Give Space My Love, An Intellectual Odyssey with Dr. Stephen Hawking
Bibliographic record
Abstract
This book is a record of my dialogues with Stephen Hawking, his graduate assistants and his nurses during a four city public lecture tour I organized for Hawking, including Portland, Eugene, Seattle, Vancouver, BC. We discussed 20th century science and philosophy of science. Since I was often the one being questioned, much of the contents reflect my PhD research at the University of London. My focus was on understanding the limits of science, as represented by quantum theory and relativity. My mentors had been Paul Feyerabend and Imre Lakatos, and I was strongly influenced by Karl Popper and Thomas Kuhn. In one in depth presentation to Hawking I suggested that Newtonian space-time and Maxwellian space-time were complementary, were defined by complementary symmetry principles. I had opportunity to present the same arguments to Kip Thorne and Freeman Dyson. Hawking simply remarked that I 'may be right'. Thorne confirmed that practitioners of General Relativity use both depending on the problem at hand. Dyson was emphatic – "Yes. Definitely. Absolutely."
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.024 |
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".