Bibliographic record
Abstract
A large number of legal concepts is expressed through metaphors, exemplifing the Conceptual Metaphor Theory created by Lakoff & Johnson. Indeed, the law often resorts to metaphors in order to allow us to understand an abstract and/or unknown concept in terms of another that is concrete and/or familiar (the metaphor of the “living tree” to describe some aspects of the Canadian constitution is a case in point). The law itself is often compared to an object (“to break the law”, “a law breaker”) or to a person (“Our Lady the Common Law”, “the arm of the law”, “the eye of the law”). What is more, some metaphors have allegedly contributed to developing new legal concepts (for instance the metaphor of “the golden thread” was used to evoke the then new notion of the presumption of innocence in Canada).However, though it cannot be denied that metaphors are useful to shed light on legal concepts, the interpretation of the latter is necessarily biased because the compared concept is always circumscribed to the comparing concept which, besides, tends to present the interpretation as the only possible one. This way, some metaphors can be used as manipulative tools.Finally, the cognitive function of metaphors may be limited: on the one hand, some metaphors may remain obscure even to the native speaker (“blue sky law”, “thin skull doctrine”), on the other hand, others may be misleading either because they are ambiguous or because they suggest (impose?) one vision of the world that excludes all the others.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.024 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".