Bibliographic record
Abstract
The nonuniversality in computation theorem (NCT) states that no computer capable of a finite and fixed number of basic operations per time unit can be universal. This result, obtained in 2005, disproves major prevailing dogmas in computer science, on the strength of several counterexamples that differ significantly from one another. Thus, the NCT shows that the Church–Turing thesis (CTT) is false: It is not true that the Turing Machine can execute any computation possible on any other computer. It also disproves the inflated CTT, whereby there exists a universal computer, that is, a physical device capable of carrying out any computation conceivable. At the heart of the NCT is a refutation of the simulation principle, which states that any computation possible on a general-purpose computer can be simulated, more or less efficiently, on any other general-purpose computer . While more than 10 years have now elapsed since the NCT was established, the result is still widely misunderstood. This state of affairs is due to a number of misconceptions about the nature of the counterexamples and the significance of the NCT. The purpose of this paper is to dispel these misconceptions and convey the simple, yet important idea that universality in computation cannot be achieved, except by a computer capable, each time unit, of an infinite number of basic operations executed in parallel.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".