Bibliographic record
Abstract
For some, Vancouver's Downtown Eastside is notorious for its mental illness, homelessness, and most importantly, its drug scene.Drug use and addiction plagues numerous lives and it does not distinguish between age, gender or socio-economic status.To better understand the motivators behind drug use, desistance and sobriety, qualitative, semi-structured interviews were conducted with 12 participants who previously used drugs, participated in that the drug scene, and sought treatment from a Downtown Eastside treatment organization.Using the principles of the developmental and life-course theories, this study uncovers that there are numerous factors that lead an individual into drug dependency, such as the lack of parental bonding resulting from early childhood trauma and the lack of pro-social skills; thus treatment is effective if it addresses those shortcomings.In essence, treatment is a time of self-transformation, where an individual is given tools to develop responsibility and accountability.With significance placed on those tasks, and the fear of loosing that responsibility, motivation for achieving and maintaining sobriety is achieved.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".