Bibliographic record
Abstract
Purpose The purpose is to show the importance of language in economic activity and encourage economists to pay more attention to language than hitherto. Design/methodology/approach The paper examines John Searle's work and consider its implications for economic theory. It discusses Searle's “background” and its role in the explanation of intentional phenomena. It analyses the implications of the background for the notion of rule‐following and illustrates the role the background plays in decision making by examining conversation‐analytical studies of decisions involving responses to invitations. Findings Searle offers a novel interpretation of rules which contrasts to that found in economic theory. The decisions I examine manifest a “preference structure” independent of the preferences of individuals. These can be called “background preferences”. Personal and background preference rankings can conflict with each other. This leads to a possible interpretation of the phenomenon “weakness of will”. The paper concludes with remarks on why language is a neglected phenomenon in economic theory. Originality/value Searle's work is slowly coming to the attention of a few economists. However, most contributions to the debate so far are at a very general level and do not tackle concrete issues of economic theory. By looking empirically at decision making, this paper shows the relevance of Searle to an issue at the heart of economic theory.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".