The Process of Speech-acting Specifies Methods for Grasping Meaning. Ten Operations. A Contribution to Hermeneutics
Bibliographic record
Abstract
How does speech-acting theory explain thegeneration of meaning and meaningful collective action? What place does firstperson subjective experience have in the theory? What are some of themethodological implications of the theory? The purpose here is to outline the Searlean theory of meaning formationand to draw some directions for research into meaning formation andorganization from that outline. Searle assumes a deep intentionality, adirectedness towards the world of all the human capacities. Searle asks: how dohumans from external inputs from the world and through language produceknowledge of the world and organized projects that implemented change theworld. Reasoning implies meaning. Reasons to act identify conditions of success= a meaningful act. Research directions (10) are drawn from elements of the speech act theory: the locutionary process, status assignments and meanings,willfulness, types of speech acts, decision-making and organization.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.041 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".