Bibliographic record
Abstract
I begin my address by identifying three vertical directions of reductionism (upward, horizontal, and downward) with ontological (descriptive) and epistemological (explanatory) forms. Following a brief discussion of horizontal reductionism, I deal with upward reductionism in terms of postmodernist thought and its influence on social scientists. In my discussion of downward reductionism, I reject ontological (descriptive) reductionism while embracing a qualified version of epistemological (explanatory) reductionism. While admitting psychological and biological variables to explanation of social phenomena, this qualified version of explanatory reductionism maintains that individual-level variables cannot explain social phenomena without residue because such phenomena have emergent properties. I support my argument by discussing the perceived threat of neuroscience by psychologists and the lesson it holds for sociology and social psychology with respect to employing individual-level explanatory variables. Following a discussion of affect control theory and cultural sociology as examples of the qualified version of explanatory reductionism advanced in this address, I present a model of an integrative social psychology, locating different kinds of reductionism and social psychological theories in this model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.051 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| 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".