Bibliographic record
Abstract
The global approach to cancer control must not hide the gross inequalities in cancer prevention and control. One of the reasons for these inequalities is the deficiency in knowledge creation in low-income and middle-income countries because it is the knowledge generated by research that will drive the innovations for cancer control. The knowledge ladder is made up of data, information, knowledge, and wisdom. I describe crucial areas of research-surveillance systems, implementation research, and research that involve the application of behavioural economics. There is no ideal taxonomy of innovations, but a simple one is offered: supply and demand or need-driven innovations. The supply-driven innovations employ technology and devices. Innovations driven by demand and need include social innovation and innovation to address an unattended need, especially in the poor. The weakness of surveillance systems and cancer registries, despite their importance for surveillance, might be a reflection of the fact that health systems on which the dissemination and implementation of innovations depend are open systems, are counterintuitive, and resist incorporation of policies or other inputs that could change them. At the Toronto Global Cancer Control Conference, March 1–3, 2018, we will examine the multisectoral approach to cancer prevention and control, with reference to the triple-helix model of innovation, which involves academia, business, and the government, and the growing interest in public–private partnerships. Suggestions will be made for enhancing visibility of the problem, strengthening and widening the epistemic community, and for framing the issue not only as a matter of welfare but as one of utility. I declare no competing interests.
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 | 0.053 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".