Bibliographic record
Abstract
This script is the outcome of a practice-led research project exploring the exponential growth in British Columbia of fentanyl-detected illicit drug overdose deaths. A September 2017 summary from The BC Coroners Service’s confirms a 143% increase in the number of deaths. In the canon of dramatic screenplays, few scripts explore the domain of drug addiction exclusively as a subject. In The Man with the Golden Arm (1955), Frank Sinatra plays Frankie Machine, a drug addict who becomes clean in prison and yearns to be a drummer. Yet, his heroin addiction is never specified. In Clean and Sober (1988) Michael Keaton is an alcoholic and cocaine addicted realtor who hides in a treatment center. However, in the recent, Emmy Award winning television series, Breaking Bad , Walter White’s sidekick, Jesse Pinkham, is clearly a heroin addict. My screenplay differs from the above dramas in that most characters are not confirmed addicts. In my dramatized script I portray characters from many walks of life: teenagers, parents, actors, politicians, and the homeless, and show how they struggle with the impact of illicit drug usage. It can happen to anyone. As a character states in Way Five, “It (meaning addiction) is a health issue.” Rather than a screed about illicit opioid use, my screenplay attempts to inform, open conversations and transform attitudes.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".