Bibliographic record
Abstract
The legal profession is not the most beloved of vocations. From Plato through William Shakespeare and Charles Dickens to Tom Wolfe and John Grisham, literature attests to its impugned status. Matters are not much different in real life. In the public's mind, lawyers are perceived as being not only adept at the dubious arts of manipulation and double dealing but also moral hypocrites because they defend these practices in the brazen name of ‘professional ethics’. Along with used car dealers and telemarketers, lawyers are considered to be among the least trustworthy and least respected of all professionals. Lord Bolingbroke's assessment of the legal profession several centuries ago remains true today: ‘the profession of law, in its nature the noblest and most beneficial to mankind, is in its abuse an abasement of the most sordid and pernicious kind’. If the legal profession at large suffers from bad press, the criminal bar is the butt of the most insistent criticism. The ethical history of criminal lawyering is populated by a cast of colourful characters – from the ennobled image of Clarence Darrow to the more dubious persona of Johnnie Cochrane. People tend to identify defence lawyers with their unsavoury clients and their unforgivable deeds. However, when individuals are in trouble, they want the best and most dogged lawyers on their side and their side alone; they need to be assured about the unquestioned loyalty of their lawyer to their cause. Whatever the rap against the legal profession generally, accused persons want their lawyer to do all that they can to raise every issue and argument in an uncompromising way that affords them every chance of acquittal. This push and pull puts criminal defence lawyers in an ethical and professional bind. They must juggle competing obligations to their clients, the courts, the legal profession and the public interest. And this is no mean feat. Often misunderstood in actions and motivations, criminal lawyers are there to defend their clients, not to judge them. Indeed, the mantra of the defence bar is that they ‘lend their exertions to all, and themselves to none’. Because accused persons are presumed innocent and entitled to a fair trial, the lawyer is required to challenge the Crown's case in the most vigorous and partial way.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".