Four Galore? The Overlap between Mary Douglas’s Grid-Group Typology and Other Highly Cited Social Science Classifications
Bibliographic record
Abstract
Recently, neuroscientists have argued that elementary ways of organizing, perceiving, and justifying social relations lurk behind the diversity of social life. In developing grid-group typology, anthropologist Mary Douglas proposed such universal forms. If these are universal, then we could expect other widely cited classifications to overlap with grid-group typology. We tested this expectation by examining to which extent the elements of Douglas’s typology overlap with those of 39 highly influential classifications proposed since 1970. We established overlap by calculating the interrater agreement among 11 coders. Fair to good interrater agreement, despite a complex coding exercise and minimal training, suggests that such overlap exists. Nevertheless, limits to our research design call for further studies. These findings should contribute to a rekindling of the question whether universal forms of organizing and perceiving social relations exist and to a further consideration of whether Douglas has managed to uncover these.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".