Bibliographic record
Abstract
There is little debate that American society has undergone monumental changes in the last thirty-plus years.Societies, of course, are always changing.But now, the speed and span of change can be disorienting, challenging longestablished traditions, institutions, understandings, norms, and ideas at their very core.Much of the change today has been enabled and compelled by breakthroughs in information technology.Rapid advances in technology are driving new ways of relating to one another, creating entirely new industries, challenging old wmarketx occupants, breaking down geographical barriers, opening prospects for greater selfservice, disrupting social, political, and economic orders, and recalibrating public expectations of institutions, both old and new.The famous Canadian philosopher Marshall McLuhan noted that wAs technology advances, it reverses the characteristics of every situation again and again.The age of automation is going to be the age of ydo it yourself.zx 1Futurist Ray Kurzweil observed that the human brain is whardwiredx to think about the future in linear terms, while the impacts of information technology are exponential. 2wThirty steps linearly, thatzs our intuition, gets us to 30.Thirty steps exponentiallyv2, 4, 8, 16vgets us to a billion.x 3 Five of the six most valuable companies in the world occupy the information technology space, with the oldest of those five companies, Microsoft, being founded in 1975. 4 Exponential change is all around us, and it is not limited to the evolution of computing power or the development of new apps that connect people in new ways.Exponential change is occurring across a range of matters, reshaping relationships, our expectations of institutions, core social and legal values, and even our traditional understanding of the foundation of the public justice system.So how does the public justice system, a traditionally linear institution, keep pace with a society undergoing exponential social change?One way might be to rethink what it means to go to court by, for example, broadening and integrating various dispute resolution services into the * Executive Vice-President, National Center for State Courts.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 0.000 |
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 teacher head, 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".