Summary Legal and Technical Report on Spent Convictions
Bibliographic record
Abstract
This legal and technical report on Spent Convictions modelling summarises the findings and results already presented in Deliverables DC3.1 - DC3.6 and provides concluding perspectives. This report includes a brief overview of the preliminary conceptual work and notes on the Spent Convictions Scheme solution prior to its semi-automated modelling. This report should ideally be read in conjunction with the earlier project deliverables: DC3.1 introduces the subject; DC3.2 presents the clustering for the survey on legal compliance; DC3.3 presents the roadmap towards publishing law as data using Natural Language Processing (NLP) tools; DC3.4 describes in more detail the Spent Convictions Scheme; DC3.5 elaborates on the potential interpretative issues and impact of Crimes Act 1914 (Cth) (Part VIIC – Division 3: Sections 85ZV, 85ZW and Associated Definitions); and DC3.6 analyses the case law perspective. This report briefly discusses (i) the survey on legal compliance in which the difference between regulatory and legal compliance is grounded; (ii) the legal issues raised by the Spent Convictions Scheme (steps, interpretations, case law and privacy); (iii) the Spent Convictions Scheme modelling in defeasible semantic logic, (iv) and Natural Language Processing (NLP) techniques and applications. D3C.7 therefore summarises the results and findings of the Project and offers a proof of concept.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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; both teacher heads agree on what is shown here.
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".