Bibliographic record
Abstract
Abstract In this chapter I explain the rationale for having a cultural defense. The entire argument hinges on the idea that culture shapes the identity of individuals, influencing their reasoning, perceptions, and behavior. I begin with the concept of culture and some of the problems associated with the analysis of culture. This is followed by a brief discussion of some of the scholarship by anthropologists and social psychologists concerning the manner in which culture affects individuals, especially the concept of enculturation. Next we consider the implications of culture for law: if culture affects motivations, what does this mean for the functioning of the legal system? In this study I am not concerned with high culture (as in opera, museums, and so forth) or mass or popular culture (as in comic books, films, etc.). Instead, when I refer to culture, I mean traditional culture, a synonym for a way of life. As a working definition, we may use a formulation of the Canadian UNESCO Commission: Culture differs from society inasmuch as culture is an abstraction, whereas society is the collection of individuals in the community. This means culture is invisible and society is visible.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".