Bibliographic record
Abstract
Much has been written about how automation will change the legal profession as a whole, less so about how automation might affect women in legal practice. This paper briefly maps the likely changes that legal tech (legal technology) will bring to the provision of legal services, and explores how these changes might affect the barriers to advancement that women face in the profession. It determines that, while the use of legal tech may improve women’s work/life balance and overall job satisfaction by bringing about more flexible working hours, positive changes to the billing hours’ system, and fairer hiring and promotion mechanisms, an unfettered inclusion of legal tech might lead to increased working hours for less wages, increased competition for case files among associates, and the perpetuation of existing gender biases when using algorithms in the hiring and promotion process. Finally, the paper makes several recommendations on how law societies, bar associations and other relevant regulatory bodies could ensure that legal tech promotes rather than hinders Equality & Diversity in the legal profession. It proposes that: (1) detailed data on men and women lawyers should be collected to better inform equality and diversity policies; (2) law firms should be required to report on their progress in pursuing equality and diversity; (3) management techniques to promote work/life balance and more flexible pricing systems should be encouraged; (4) female entrepreneurship in legal tech should be promoted; and, (5) technological due process procedures should be required when using algorithms in law firm management to ensure fairness, accuracy and accountability.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".