Implementing the Precautionary Principle through Stakeholder Engagement for Product and Service Development
Bibliographic record
Abstract
The precautionary principle is a sustainable development principle that attempts to articulate an ethic in decision making since it deals with the notion of uncertainty of harm. Uncertainty becomes a weakness when it has to serve as a predictor by which to take action. Since humans are responsible for their actions, and ethics is based in action, then decisions based in uncertainty require an ethical framework. Beyond the professional deontological responsibility, there is a need to consider the process of conception based on an ethic of the future and therefore to develop a new ethical framework which is more global and fundamental. This will expose the justifications for choices, present these in debates with other stakeholders, and ultimately adopt an axiology of decision making for conception. Responsibility and participative discourse for an equal justice among actors are a basis of such an ethic. By understanding the ethical framework of this principle and applying this knowledge towards design or innovation, the precautionary principle becomes operational. This paper suggests that to move towards sustainability, stakeholders must adopt decision making processes that are precautionary. A commitment to precaution encourages a global perspective and the search for alternatives. Methods such as alternative assessment and precautionary deliberation through stakeholder engagement can assist in this shift towards sustainability.
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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 | 0.095 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".