Bibliographic record
Abstract
There are certain areas of law where it assists a scholar (and perhaps to an extent a lawyer) to have an overtly and directly personal stake in the legal discussion or debate in which he or she engages. When engaging in such a discussion in this personal way, the participant uses a "direct" voice. To be distinguished from this type of participant is a person who, while interested intellectually or politically, does not have the same personal stake in the outcome of the discussion or debate. This person has an "indirect" voice; in fact, in most legal discussions, most participants have indirect voices. Legal discourse is historically characterized by this detached perspective. The indirect voice is the ordinary role for both the lawyer and the scholar in legal debates and discussions. In this essay, the author reflects on the advantages and disadvantages of the direct voice. He explores some of the unrealistic assumptions associated with the direct voice and the consequent limitations on the voice. In addition, he considers how the role and even the need for a direct voice changes as the discussions in which it participates conclude or the context in which the discussions occur alter.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".