Institutions, Institutional Change, Language, and Searle
Bibliographic record
Abstract
This paper endeavours to contribute to the growing institutionalist literature on the conception of the institution. We draw from John Davis’ (2003) analysis of the individual in posing the questions: what differentiates institutions, and how can changing institutions be identified through time and space? Our analysis develops Searle’s (2005) argument that language is the fundamental institution. Searle’s argument is rather functionalist, however, and does not convey the ambiguity of language. Moreover, language and understanding, surely when related to most institutions in real life, delineate and circumscribe a community. A community cannot function without a common language, as Searle argued, but language also constitutes a community’s boundaries, and excludes unsavoury outsiders or alien topics for discussion. This is how institutions both constrain and enable. By drawing upon Luhmann’s (1995) systems analysis and notions of discourse, communication, and text we aim to augment the existing analytical role ascribed to habit in institutional analysis. Thus, we submit, understanding institutional change and thus durability may progress.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".