Are Legal Texts Grey Literature? Toward a definition of Grey Literature that invites the Preservation of Authentic and Complete Originals
Bibliographic record
Abstract
Legal texts, though they exist in a wide variety of forms, are most typically thought of as Law Books. Law books, hardbound volumes in expensive bindings of browns and blacks, are heavy, difficult, and technical. They are a prop to popular conceptions of the law itself, and resemble more closely than most other earthly books the Platonic form of the ‘weighty tome.’ In fact, some law libraries do a regular, if not exactly brisk, trade in renting their law books to TV and film productions. And the more dour the entertainment, the more likely it is to include law books in the background. Perry Mason was too active a man to spend much time in his law office, so we did not see the floor-to-ceiling oak shelves of reserved and wise volumes which undoubtedly offered him nightly counsel. In the ‘80s, LA Law was too concerned with exciting power suits and hairdos to have much need of our serious friends. The ‘90s brought us Law and Order, the dourest thing Americans have thus far been able to stomach on a regular basis. Much like marmite in the UK, or beef jerky in Canada, this distinctive fare seems to have become a regular and much-loved part of the daily American diet. As a result, US citizens, and those of us in the provinces of the empire as well, are treated to regular glimpses of the formidable law book in law office scenes. I would guess that Law and Order also offers the very occasional library scene, though I’m not enough of a devotee to attest to this “so help me God” on a stack of Bibles.
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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.015 | 0.091 |
| Scholarly communication | 0.040 | 0.042 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".