Ontologies on trial: the lesson of<i>Maurice</i>v.<i>Judd</i>(New York, 1818)
Bibliographic record
Abstract
Ontologies, and legal ontologies are a particular class of application of these, have become fairly popular. Are they fit for purpose? Like with all kinds of tools from legal computing, one must be cautious, and consider very attentively what the likely receptions are going to be, among users. Maurice v. Judd (New York, 1818), when a jury was called to decide whether whale oil is fish oil, and decided that indeed whales are fish, is a trial that was analysed in Graham Burnett's Trying Leviathan: The nineteenth-century New York court case that put the whale on trial and challenged the order of nature (Princeton, NJ: Princeton University). It is a highly readable book, and it has something important to teach developers of ontologies in the legal domain. The intended public of users of any software, or of ontologies in particular, is paramount. Those intended users you are catering to with your new tool are going to make or break it, just as it happened, e.g. to sentencing information systems in Canadian provinces.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.040 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".