Bibliographic record
Abstract
In this paper, we reconsider Marcel Mauss’s concept of the ‘total social fact’ (TSF) and its potential to redefine the sociological analysis of various phenomena. The prevailing interpretation of TSFs in the literature, which we refer to as the ‘Social Totality’ approach, focuses on phenomena sitting at the intersection of multiple social sites or spheres. We argue that there is an alternative interpretation, in which the Maussian TSF is intended to denote a tripartite framework for explaining phenomena at the intersection of the biological, the psychological, and the social, which we term the ‘Human Totality’ approach to TSFs. The Human Totality approach aligns more closely with current trends in contemporary sociology, which are increasingly incorporating insights from embodiment, psychology, neuroscience, and cognitive science. We illustrate the analytic utility of the Human Totality approach by examining the phenomenon of addiction as a Maussian TSF, showing how its biological, psychological, and social dimensions intersect and interact. We close by noting how the Human Totality conception of TSFs could lead to a more holistic and robust sociological science by bridging explanatory gaps between different intra and interdisciplinary perspectives.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.054 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".