Bibliographic record
Abstract
Social media is an area of growing importance in modern life where more discussion, reading, and understanding of legal issues is occurring by members of the public. Lawyers have been cautious to embrace this new medium, for both technological and ethical concerns. One of the biggest worries attached to professionals interacting on social media is confusion over the identity of the space. Is social media the new public square, where people from all walks of life gather? Or is it a place for friends and close contacts to interact with each other. Is short, is social media a public or a private place? A well informed family law practitioner should be able to effectively manage the risks involved in the use of social media by both their clients and their staff. Social media can operate as an effective means to enhance a practice, and more and more clients will turn to the Internet to learn about their family lawyer before hiring them. The modern family law client faces a problem of relevancy to the public, especially in face of changing client expectations.Not only can family lawyers use social media to keep themselves properly informed, but they can help demonstrate their currency to potential clients through it.
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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.008 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".