Bibliographic record
Abstract
I will now ask you to forget all about replicators and lumbering robots, algorithmic processes and the unfeeling universe. Discussing these issues was important in order to see how human-level reality can survive even the starkest version of physicalism as a global explanation for our universe, but there are at least a couple of reasons why physicalism will usually play no immediate role in our work. First of all, it is entirely possible that, at some point in the future, we will find ourselves having to modify or even abandon strong physicalism. A colleague of mine, a renowned social psychologist, is currently devoting a significant portion of his lab resources to proving that human consciousness constitutes a full-blown fifth dimension of reality, a claim he intends to substantiate in part by demonstrating the reality of precognition (ESP). Although they are not yet published or widely peer reviewed, he claims to have replicated results where events that have not yet happened are exerting a priming effect on subjects: that is, the subjects are apparently showing signs of being primed by stimuli that they will see in the future. He anticipates a great deal of resistance to his theory – which, incidentally, is one reason he did not begin pursuing it until he had a secure day job – and for good reason: it goes against everything else we know about how the universe works.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.172 | 0.073 |
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".