Bibliographic record
Abstract
Constructivist (constructionist) epistemologies focus on ethics as a system of values in the mind—even when previously co-constructed in a social context—against which social agents compare the actions that they mentally plan before performing them. This approach is problematic, as it forces a wedge between thought and action, body and mind, universal and practical ethics, and thought and affect. In this contribution, I develop a transactional approach to ethics that cannot be developed within constructionism. In a once-occurrent world, every act is ethical because it has consequences for the agent (who affects and is also affected), and the world as a whole. However, whereas many in the social sciences continue to articulate a theory of action and thus the practical nature of ethics in terms of the individual's act, in this contribution I show that the act always already is spread across people and things and, thus, is an integral and constitutive part of a transaction. This utterly relational take on human behavior thus undermines approaches to ethics that are based on the individual as unit of analysis. Most controversially perhaps, I exhibit how those who may feel hurt by the actions (talk) of others are themselves agents affecting those others and, thus, answerable. This approach is illustrated in the context of power-knowledge, which is the result of an always ongoing, only once-occurrent situated and situational struggle rather than something a priori distributed between people.
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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 | 0.064 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.084 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".