The Future of Online Legal Journalism: The Courts Speak Only Through Their Opinions?
Bibliographic record
Abstract
It is hard to avoid hyperbole when discussing the current state of the media and what the future holds. Even when describing, in 1964, the relatively boring media landscape at the time—compared to today—the iconic Canadian media theorist Marshall McLuhan wrote: “After three thousand years of explosion, by means of fragmentary and mechanical technologies, the Western world is imploding.”1 What would McLuhan say today? If the Western world was imploding in 1964—when the Internet was not even a dream, and the 8-track tape and Betamax had not even been invented, much less gone extinct— what do we make of today’s media revolution? What does the future hold? This article is intended as a snapshot of the media revolution as it affects one critically important slice: legal journalism. My perspective is that of a former journalist who has spent the past nine years as the chief communications officer of the Supreme Court of Ohio. 2 Specifically, this paper examines: (a) how overall trends in the media are impacting coverage of the courts, resulting in a decline in quantity and quality of court coverage; (b) how this is a matter of great concern when it is understood that the media have always played a critical role
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.035 | 0.062 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".