Bibliographic record
Abstract
Framing the Opioid Crisis in Canada empirically examines public debates about the opioid crisis by politicians, journalists, and the general public, focusing on who they blame for the crisis and their proposed solutions. Delving into the complex public discourse surrounding one of the most pressing health crises in Canada, the research investigates how politicians, journalists, and the general public attribute responsibility for the opioid crisis and the solutions they propose. By employing a mixed method approach including the use of various digital tools, this book addresses critical gaps in understanding the opioid crisis. Who do these actors blame for the opioid crisis, and what are the differences and similarities in their perspectives? How have their sources and narratives evolved over time? What solutions do they propose, and how have these solutions changed? Revealing a polarized discussion and crisis communication that often overlooks the urgent needs of drug users, this is essential reading not only for Canada but also for international audiences facing similar challenges. The multidisciplinary approach provides valuable insights for different academics, health professionals, and policymakers for a better understanding of public health crises worldwide.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".