Bibliographic record
Abstract
To the Editor: Promoting the need for further research in the face of demonstrated harms from exposure in communities elsewhere is premised on policymakers’ claims that policy action cannot be justified until effects seen elsewhere are demonstrated locally. This argument must be recognized for what it usually is: namely, a delay tactic to maintain the status quo. I suggest that, with proper community engagement, the need for research instead of action would be exposed early on and thus be obviated. This said, Savitz1 is to be commended for his commentary in EPIDEMIOLOGY. He advises us, before embarking on a study, to ask “To what end?” Equivalently we might ask: “In whose best interests?” The researcher’s passion to conduct research must be balanced against local community interests. The International Society for Environmental Epidemiology’s (ISEE’s) 2012 Ethics Guidelines2 address, in some depth, the need for community engagement as integral to respecting community interests. From the core values expressed in ISEE’s 2012 Ethics Guidelines, “… Our duty as scientists is to do the best science possible with a view to reducing uncertainties. However, the presence of uncertainty is no justification for inaction in the face of environmental harms.”2,3 The Precautionary Principle is applied where there is scientific evidence of potential harm, but the matter may be considered by some to be unsettled as to causation. There is no justification for delaying action to protect exposed populations, especially if the agent has already been shown to cause adverse health effects in other exposed populations. Furthermore, there are pollution episodes which involve population exposures to known toxicants/carcinogens, but the specific health outcomes are rare or have not been adequately studied. In these situations, it is necessary to extrapolate from knowledge that we have about the agents to protect the public, again invoking the Precautionary Principle. A worthy component of the action plan may include prospective medical monitoring and surveillance of the exposed population; this, instead of undertaking a potentially inconclusive etiologic study that would only delay needed action. ACKNOWLEDGMENT The author thanks Dr. Shira Kramer for her constructive review. Colin L. Soskolne University of Alberta Edmonton, AB, Canada Health Research Institute University of Canberra Canberra, ACT, Australia [email protected]
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.265 | 0.175 |
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".