Finding the right frame: What is the problem represented to be in a national tuberculosis strategy
Bibliographic record
Abstract
This paper examines how a problem representation that targets specific populations impacted the formulation of a national tuberculosis strategy in Canada. Globally, one third of the world population is infected with tuberculosis (1). Tuberculosis has garnered increased international attention with the emergence of multi-drug resistant TB. International standards endorse that low incidence countries enact TB policies that address the most vulnerable and hard to reach groups (2). In Canada, a low incidence country, two groups are disproportionately affected by TB: the Aboriginal and foreign-born populations (3). In 2014, Health Canada and the Public Health Agency of Canada released a national strategy for TB (3) . This analysis of the “Framework for Action” policy document will focus on problem framing using Bacchi’s theory of “What’s the problem represented to be?” (4) The choice of a problem frame reduces the complexity of tuberculosis and its political and social connections. Specifically, problem frames containing target populations are influenced by the social constructs of that population. This paper will argue that narrowing the frame of tuberculosis to a development issue in the Aboriginal population and a security issue for the foreign-born population has implications at the domestic and international level.
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 | 0.087 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".