Ethics in Research on Learning: Dialectics of Praxis and Praxeology
Bibliographic record
Abstract
Qualitative social research designed to develop ways of understanding and explaining lived experience of human beings is a reflexive human endeavor. It is reflexive in that as researchers attempt to better understand their participants, they also come to better understand themselves. Consequently, research ethics itself becomes an ethical project, for it pertains to participant and researcher at the same time: Both are subjects, knower and known. Particularly in case of research on learning, reflexivity arises from the fact that the research itself constitutes learning about learning. How is ethics in research on learning reflexive of, in its praxis and praxeology, ongoing events and changes of the human learning? In this study, from our experience of conducting a project designed to inquire into "learning in unfamiliar environments," we develop pertinent ethical issues through a dialectical process—not unlike that used by G.W.F. HEGEL in Phenomenology of Spirit—grounded in our lived experience and developed in three theoretical claims concerning a praxeology of ethics. First, ethics is an ongoing historical event; second, ethics is based on the communicative praxis of material bodies; and third, ethics involves the creation of new communicative configurations. We conclude that ethics is grounded in a fundamental answerability of human beings for their actions, which requires communicative action that itself is a dialectical process in opening up possibilities for acting in an answerable manner. URN: urn:nbn:de:0114-fqs0501198
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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 | 0.110 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.165 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".