Conceptualizations of Knowledge in Structuring Approaches to Moral Development: A Process-Relational Approach
Bibliographic record
Abstract
Like other aspects of child development, views of the nature and development of morality depend on philosophical assumptions or worldviews presupposed by researchers. We analyze assumptions regarding knowledge linked to two contrasting worldviews: Cartesian-split-mechanistic and process-relational. We examine the implications of these worldviews for approaches to moral development, including relations between morality and social outcomes, and the concepts of information, meaning, interaction and computation. It is crucial to understand how researchers view these interrelated concepts in order to understand approaches to moral development. Within the Cartesian-split-mechanistic worldview, knowledge is viewed as representation and meaning is mechanistic and fixed. Both nativism and empiricism are based in this worldview, differing in whether the source of representations is assumed to be primarily internal or external. Morality is assumed to pre-exist, either in the genome or the culture. We discuss problems with these conceptions and endorse the process-relational paradigm, according to which knowledge is constructed through interaction, and morality begins in activity as a process of coordinating perspectives, rather than the application of fixed rules. The contrast is between beginning with the mind or beginning with social activity in explaining the mind.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.021 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".