Mikhail Golichenko interview, Oakville, ON, Canada, 2022
Bibliographic record
Abstract
Mikhail Golichenko, Oakville, Ontario, Canada, 2022 June 2, oral history interview, by Lillie Guo, Los Angeles, California, USA. The inteview was conducted over the Internet (Zoom) as part of the Health and Human Rights Oral History Project, Institute on Inequalities in Global Health, University of Southern California, Los Angeles, California, USA.Mikhail Golichenko is a Russian/Canadian lawyer and a Senior Policy Analyst at the HIV Legal Network, where he leads human rights research and advocacy in countries of Eastern Europe and Central Asia, with a particular focus on drug policy issues. He previously served with the United Nations Office on Drugs and Crime, UN Peacekeeping in West Africa, and the Russian police service. In his oral history, Golichenko charts the development of his health and human rights career and how he increasingly became involved in the harm reduction space. He discusses the challenges and opportunities of using the law as a tool of social inclusion for people who use drugs, and how geopolitics have affected evidence-based harm reduction interventions in countries of the former Soviet Union.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.073 |
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".