Acknowledgments

AI assistance

MORIE was developed with substantial assistance from frontier AI assistants. The author retains full responsibility for the code, the methods, and the scientific claims. AI assistance accelerated implementation but does not change the attribution of the work.

This disclosure is included for transparency; it does not transfer authorship, copyright, or licensing obligations away from the human author. Where AI-generated code reproduces material from training data verbatim, the upstream licence governs that material; the author has reviewed the source for any such cases.

Anthropic Claude

Anthropic’s Claude family — Opus, Sonnet, and Haiku across the 4.x generation — was used extensively throughout development for code generation, refactoring, documentation, code review, and design discussions. Use was supported by the Anthropic research-credit program.

Project URL: https://www.anthropic.com/claude

Google Gemini and Vertex AI

Google’s Gemini 2.5 models — Pro and Flash — running on the Google Cloud Vertex AI platform were used extensively for additional code generation, cross-checking Claude-generated code, multi-modal data analysis, and prototype evaluation. Use was supported by the Google research-credit program.

Project URLs:

• https://deepmind.google/technologies/gemini/

• https://cloud.google.com/vertex-ai

The Anthropic and Google research-credit programs are compute- allocation programs; they do not constitute endorsement of MORIE by either company.

Funding and infrastructure

• Anthropic — Claude API research credits.

• Google — Gemini / Vertex AI research credits.

Mentorship and expert review

The author thanks Glenn McNamara — a 35-year career with the Ontario Government — for his methodological mentorship. Glenn introduced the author to foundational distribution theory and the applied-statistics intuition for administrative data that grounds much of this framework. He is the M (catalyst, for McNamara) in MRM (Multilevel Reconciliation Methodology) — the framework that powers the OTIS / SIU / TPS analyses across this package.

The author thanks Prof. Angela Zorro Medina, Centre for Criminology and Sociolegal Studies, University of Toronto, who is the author’s supervisor, methodological instructor, the domain-expert reviewer of the preliminary methodological approach, and a knowledge user of the framework. Prof. Medina is the M (supervisor & reviewer) in MRM. The methodological lineage MRM follows is established in her work on anti-gang legislation:

Zorro Medina, Á. (2023). The Effect of Anti-Gang Laws on Crime and Social Control. University of Chicago Job Market Paper. https://azorromedina.com/wp-content/uploads/2023/08/JMP_ZorroMedina_28_08_23H.pdf

Specifically, MRM inherits from her JMP:

• a staggered two-way-fixed-effects identification strategy with formal leads-and-lags Granger-causality diagnostics for the parallel-trends assumption (Athey & Imbens, 2022; Callaway & Sant’Anna, 2021; Goodman-Bacon, 2021);

• an explicitly multi-source data-integration pattern — her five U.S. sources (FBI SRS/NIBRS, BJS BJSPS, FBI UCR, BLS, state annotated criminal codes) are structurally analogous to MRM’s five Canadian sources (OTIS / SIU IAP / Ontario SIU / TPS / CCRSO);

• the deterrence / routine-activities / certainty mechanism categorisation that grounds individual estimands in sociolegal theory rather than statistical fit alone;

• the inequality-effects-of-criminal-law-expansions framing (§2.3 of the JMP) that connects empirical estimands to racial and social inequality in carceral outcomes.

Her substantive review of the OTIS Major Research Paper insisted on quantitatively-grounded sociolegal mechanism, on a negative-binomial mixed model with regional-cluster random intercepts as the principal aggregate-level estimator, and on a disciplined empirical structure. That review directly shapes the methodology reported here.

Data acknowledgments

Several MRM analyses use Statistics Canada and Health Canada Public Use Microdata Files (PUMFs):

• CCS — Canadian Cannabis Survey (Health Canada, annual since 2018; multiple cycles 2018-2024).

• CSADS — Canadian Student Alcohol and Drugs Survey (2021-22, 2023-24; previously Canadian Student Tobacco, Alcohol and Drugs Survey / CSTADS, 2014-2022; previously Youth Smoking Survey / YSS, before 2014).

• CSUS — Canadian Substance Use Survey (2023, 2025).

• CADS — Canadian Alcohol and Drugs Survey (2019; https://doi.org/10.25318/132500052021001-eng).

• CPADS — Canadian Postsecondary Education Alcohol and Drug Use Survey (2019-20, 2021-22).

Although the analyses use Statistics Canada data, the analyses, interpretations, and conclusions are those of the author and do not represent the views of Statistics Canada. Public Health Agency of Canada (PHAC) and Canadian Institute for Health Information (CIHI) aggregates are used under the same standard disclaimer. Ontario open data (OTIS, A01-RCDD release; via data.ontario.ca) and Toronto Police Service open data are acknowledged with the same standard disclaimer.