Bibliographic record
Abstract
Systems thinking is gradually being rediscovered in the social sciences, and it has been enthusiastically embraced in the biosocial sciences (psychology, neuroscience, human biology) since about the mid-1990s. However, while the systems concept seems unproblematic for scholars in the biosocial sciences, it remains rather problematic for social scientists who have by and large rejected the systems theories of Parsons or Luhmann. Thus while “dynamic systems theory ” or “dynamic systems principles ” play a major role in psychology (Cervone, 2005) and neuroscience (Lewis, 2005), these phrases are almost completely absent in the social science literature. Systems thinking, to the small extent that it is practiced explicitly, can be found under the headings of “chaos theory, ” “complexity theory, ” and “sociocybernetics. ” While phrases such as “symbolic systems ” and “semiotic systems ” suggest that conceptualizing culture in systemic terms is not unusual, the more fundamental question of what cultural systems are and how they can be studied remain problematic (Tilly, 2000). This paper attempts to map out the place of “culture ” in a renewed systemic approach in light of contemporary debates in historical and cultural sociology, social psychology, and cross-cultural pragmatics. The
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.014 |
| 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".