Bibliographic record
Abstract
Abstract The suggestion that we might live in a giant computer simulation seems plausible in large part because the hypothetical sophistication of the hypothetical simulation can be increased to meet almost any objection. From an engineering standpoint, the technological increases required by this strategy may not always be feasible. Proceeding nevertheless from an idealization, David Chalmers argues that the virtual objects and worlds displayed in perfect and permanent computer simulations could be regarded as real because, on those terms (perfection and permanence), our own world could just as well be virtual. I counter that real reality, or RR, possesses (at least) five features that no VR simulation could ever reproduce: RR involves genuinely causal regularities, it is older than any machine, it will outlast any machine, it supports living bodies in ways that cannot be replaced, and thus belongs to an entirely different category than artifacts. These differences are especially robust, since they all grant the possibility of present-moment indistinguishability while halting any collapse or blurring of the virtual/real distinction.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".