Bibliographic record
Abstract
The intentional stance and the manifest image In Chapter 1, the supernatural arose because the mind-reading module could be satisfied by inputs behind which there was no obvious embodied agency, but which could nevertheless be understood as caused by some kind of mind. So supernatural entities arise spontaneously from the way mind-reading construes input. The aim of this chapter is to work out this idea and its implications for normativity and rationalization. The manifest understanding of mind-reading Mind-reading is part of a scientific psychology. But it is revealing to first analyse this capacity within the manifest image of humanity. In the everyday scheme of things, mind-reading manifests itself in how we grasp and express our understanding of each other. This intentional language attributes to people mental states not directly perceptible by the senses. Our first step is to enlarge upon Daniel Dennett's idea of the intentional stance and its context in philosophy (Dennett: 1976, 1978, 1987, 1996). This stance is the phenomenology of mind-reading. This is relevant, because religion too is a phenomenon of the conscious manifest image, however unconscious its underlying processes. But why the term “stance”? In the first instance, it denotes an explanatory strategy with respect to the prediction of the behaviour of some object – human beings are the prototypical instance. The strategy treats this behaviour as the result of rational agency.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.038 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".