Ageing disgracefully with Jude Byrne: A special section recognising her life and work
Bibliographic record
Abstract
Ageing disgracefully with Jude Byrne: A special section recognising her life and workWhile most special sections in Drug and Alcohol Review are focused on a particular research topic, this collection is devoted posthumously to a person and her interests; Jude Byrne.Unique in her ability to lead work across advocacy, policy and research, Jude used her intelligence and living experience as a person who used drugs to influence policy and practice across the alcohol and other drug, blood-borne virus and community sectors.As we noted in an earlier editorial [1], Jude's impact was significant and wide-reaching.Beginning locally, Jude was responsible for many grassroots initiatives based on local emerging community needs, including respite services for mothers and their children, and forming drug user organisations.Jude also contributed to policy and service delivery for people who use drugs (PWUD) on a global level.She pursued the ethical and methodological development of our field by representing the voices of PWUD in numerous international forums, including on World Health Organization Guidelines Development Working Groups and through her longstanding leadership on the International Network of People Who Use Drugs Board.This collection reflects the key areas influenced by her life and work.Celebrating Jude's life and work, we have also included her recorded voice.In February 2020, Peter Higgs recorded this (https://bcove.video/3KqLnCH)video interview with Jude for a conference in Manchester that was cancelled because of COVID.Cheekily titled, Ageing Disgracefully, Jude's recording reminds us that older PWUD are absent from much of the narrative about harm reduction and care.Jude describes her involvement in one of the last projects she worked on-A Hidden Population-aiming to build capacity within aged care providers to respond to the unique needs of older PWUD.We hear her powerful narrative about the fear that many people with a history of drug use have about relying on aged care services for support.Indeed, the need to include lived and living experience in academic, clinical and policy work is a thread that runs through this special section.Three of the papers included in this issue outline approaches to community-researcher partnerships.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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".