Dialectical materialist methodology for a mind-in-activity approach to work, learning and political economic consciousness
Bibliographic record
Abstract
In this theoretical review article I discuss the relationship of dialectic materialism, Cultural Historical Activity Theory (CHAT) and analyses of work, learning and political economic consciousness. The purpose is to help researchers reflect on how they might be more effective in their analytic work. Specifically, its goal is to help expand the comprehensiveness and recognition of the dynamism of phenomena of work and learning, and to support the claim of political economic consciousness as inherent to them. To do this I introduce and describe the purposes and meaning of dialectical materialist methodology. I then discuss practical procedures (‘intentional dialectics’: Ollman [(1993). Dialectical Investigations. New York: Routledge] and the ordering of these procedures (‘systematic-categorial dialectics’: Smith [(1993) Dialectical Social Theory and its Critics: From Hegel to Analytical Marxism and Postmodernism. Albany, NY: SUNY Press] in the treatment of empirical research on work and learning. Framing those discussions is a rationale for a robust appreciation of variation, heterogeneity and particularities in dialectical analysis of emergent work and learning dynamics which draws on what Adorno [(1973/2003). Negative Dialectics. London: Routledge] refers to as ‘negative dialectics’. No matter how effectively grasped, however, I maintain that dialectical materialist methodology requires suitable, substantive theory – or rather an intermediate science – such as CHAT in order to realise its full value in analyses of work, learning and political economic consciousness.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.045 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".