On the Inclusion of Emotions, Identity, and Ethico-Moral Dimensions of Actions
Bibliographic record
Abstract
With Learning by Expanding (Engeström, 1987), the development of cultural-historical activity theory entered a new phase. The book articulated a variety of structural aspects that researchers using cultural-historical activity theory might look for when attempting to analyze concrete human praxis. These aspects are captured emblematically by a triangular representation that has been a main scaffold for many scholars in their effort to understand a theory quite alien, in its dialectical foundations, to that of Western theorizing. Yet some elements of it have not yet come to be appreciated. Thus, to understand practical activity and the participative thinking that accompanies it requires understanding “the regulating effect of emotion” (Leont'ev, 1978, p. 27), because the “objectivity of activity is responsible not only for the objective character of images but also for the objectivity of needs, emotions, and feelings” (p. 54). Many scholars have focused only on the structural aspects of activity, its systemic dimensions (Roth & Lee, 2007). These scholars have not taken into account the agentive dimensions of activity, including identity, emotion, ethics, and morality, or derivative concepts, such as motivation, identification, responsibility, and solidarity – all of which are integral to concrete praxis and its singular nature. These “sensuous” aspects of activity come into focus only if the whole activity – not only its structural but also its agentive dimensions – is analyzed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".