Bibliographic record
Abstract
Language, Mind, Society Searle's work falls into three main areas: philosophy of language; philosophy of mind, including cognitive science; and social reality (Matson 2000, 586–96; Honderich 1995, 816; Guttenplan 1996, 544–50). But while a fairly distinct area, his work on social reality must be seen as a natural extension of his work on mind and, to a lesser extent, language. Searle himself summarizes his work in terms of the questions he addresses, saying that the philosophical problems that most interest me have to do with how the various parts of the world relate to each other — how does it all hang together? … The theory of speech acts is in part an attempt to answer the question, How do we get from the physics of utterances to meaningful speech acts performed by speakers and writers? The theory of the mind … is in large part an attempt to answer the question, How does a mental reality, a world of consciousness, intentionality, and other mental phenomena, fit into a world consisting entirely of physical particles in fields of force? (Searle 1995, xi) Searle's efforts regarding social reality are an attempt to answer the further question of how there can be “an objective world of money, property, marriage, governments, elections … in a world that consists entirely of physical particles” (Searle 1995, xi). His work in these areas is tightly interconnected, and his progress has been cumulative.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.155 | 0.069 |
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".