Business’ Environmental Obligations and Reasoned Public Discourse: A Kantian Foundation for Analysis
Bibliographic record
Abstract
The Kantian categorical imperative process of rational reflection and reasoned social discourse is theoretically capable of forming the moral environmental maxims applicable to business. This article argues that rational environmental discourse demands that business has an imperfect duty to develop relevant unbiased information, and perhaps to disseminate this information through participation in business-public coalitions. For the environmental problem, this “rationality” particularly concerns (i) our obligations toward future generations and distant people while recognizing that they cannot participate in current discourse, and (ii) the rules for gathering and assessing the evidence that should govern our environmental preservations and enhancements. Both these concerns demand certain scientific information requirements, as well as logical decision criteria that are perceived as stable across both overlapping generations, and affected peoples (as argued by Rawls in a different context). The criteria for Rawls’ “considered moral judgments” are shown to apply to resolutions of these business-related ethical conundrums. In a way similar to Kant’s anthropological examinations of humanity’s antisocial behaviors, this article also examines various biases that inhibit this social reasoning.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".