Does Avoiding Judicial Isolation Outweigh the Risks Related to “Professional Death by Facebook”?
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.
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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 | 0.023 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".