Bibliographic record
Abstract
Does systems theory need rethinking? Most social scientists would probably say no. It had its run, was debated critically, and found wanting. If at all, it should be treated historically. Why then might systems theory need rethinking, as the title of this symposium claims? The reason is that, unlike in the natural and biosocial sciences, any conception of system in the social sciences has remained suspect in the wake of problematic Parsonian and cybernetic systems theories. The premise of this special issue is that abandoning conceptions of systems has imposed a high price on the social sciences: a lack of ontologies and methodologies that are both philosophically profound and scientifically defensible. It has left social scientists who choose to ignore ontology in their theoretical and empirical work defenseless against enterprising settlers from a variety of humanities and social science disciplines who attack mainstream work with—often simplistic and naïve, but nevertheless fundamental—philosophical arguments, whether anti-scientific postmodernists or pseudo-scientific rational choice theorists. The goal of this special issue is to showcase new and original work that contributes to a rethinking of systems theory by taking the conception of systems seriously. This introduction offers a programmatic statement of a systemic ontology and methodology as well as a brief general outline and examples of what a systems-based approach in the social sciences entails.
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 | 0.042 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".