Bibliographic record
Abstract
The adjudication of minor crimes has long proven onerous fordefendants. Recently, however, many American jurisdictions havesupplemented the “process” burdens associated with minor crimes.They have done so by requiring misdemeanor defendants to pay muchof the signifi cant economic costs associated with the adjudicationprocess, in addition to signifi cant fi nes. These include, for example, thecosts associated with electronic tethers, “reimbursement” fees to policeand prosecutors, and participation in court-ordered programs, amongothers. Assessed in so many different forms, such costs are not fullyappreciated by misdemeanor defendants until they face the burden oftrying to pay them. Unfortunately, courts have not made any attemptto accommodate defendants’ ability to pay, instead often requiring adefendant immediately to pay a sum that is simply impossible giventhe defendant’s income. These burdens are being borne by a segmentof the population least likely to be able to bear them, as a majority ofthe misdemeanants are indigent. There are signifi cant social costs associated with this new trendin minor crime adjudication. First, there are social-welfare lossesresulting from lost wages and income tax revenues, the increased costsof new prosecutions and jail sentences imposed when costs, fees, andother economic sanctions are not paid, and indirectly the increasedcosts of public assistance for low-income defendants who lose their jobsas a result of contempt orders for their failure to pay on time. Thesecosts have to be measured against any increase in county revenuesfrom economic sanctions. But there is a larger problem as well:Courts’ recent willingness to impose greater process-oriented economicsanctions for minor crimes cannot be easily justifi ed by any of thetraditional theories of criminal punishment. That diffi culty, coupledwith the questionable social balance sheet resulting from the increasedsanctions, casts serious doubt on this emergent trend.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.032 |
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".