Bibliographic record
Abstract
The unrefined and sluggish mind of Homo Javanensis Could only treat of things concrete and present to the senses W. V. Quine Tropisms and Transitions: Some Leading Questions Before planning and reasoning, there were tropisms. Tropisms have functions and make use of information detected, but they don't, I assume, involve any actual reasoning. Somewhere along the course of evolution, and at some time in any one of us on the way from zygote to adult, some forms of detection became beliefs, and some tropisms turned into reasoned desires. And at some stage – perhaps, if Quine is right, with Homo javanensis – we became adept at processing information, that yet fell short of the power to abstract and generalize. What selective pressures can we then suppose to have effected in our brains, since then, the innovations required to bring us the capacity for fully abstract and general reasoning? I take for granted that reasoning is something we do; that much or most of what we do is influenced by emotion; that psychology is interested in everything we do; and that psychology is a branch of biology. These breezy premises raise a number of questions. What kind of connection might there be between biology and rationality? More specifically, how does the normativity of logic relate to its biological origins? Is there not, in the very idea of such a connection, something akin to the naturalistic fallacy? If our capacity for inference is in part a legacy of natural selection, are there specific emotional mechanisms that serve to influence reasoning at the proximate level? […]
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".