Does Avoiding Judicial Isolation Outweigh the Risks Related to “Professional Death by Facebook”?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
What happens when judges, in light of their role and responsibilities, and the scrutiny to which they are subjected, fall prey to a condition known as the “online disinhibition effect”? More importantly perhaps, what steps might judges reasonably take in order to pre-empt that fate, proactively addressing judicial social networking and its potential ramification for the administration of justice in the digital age? The immediate purpose of this article is to generate greater awareness of the issues specifically surrounding judicial social networking and to highlight some practical steps that those responsible for judicial training might consider in order to better equip judges for dealing with the exigencies of the digital realm. The focus is on understanding how to first recognize and then mitigate privacy and security risks in order to avoid bringing justice into disrepute through mishaps, and to stave off otherwise preventable incidents. This paper endeavors to provide a very brief overview of the emerging normative framework pertinent to the judicial use of social media, from a comparative perspective, concluding with some more practical (however preliminary) recommendations for more prudent and advised ESM use.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it