Seven key insights from critical realism and their implications for ecological thinking and action in community psychology
Bibliographic record
Abstract
This article explores some of the possible links between community psychology and critical realism, a relatively new approach to the philosophy of science that has received little attention from community psychologists. Critical realism is presented in relation to seven key insights that can be linked to fundamental tenets of the ecological approach in community psychology. These insights are: (1) A complex reality exists independently of our ideas about it, and this reality is knowable, although imperfectly. (2) Reality is composed of a complex and stratified hierarchy of open systems. (3) Causality is best understood in terms of causal processes that may or may not be directly observable or generalizable; these processes involve complex interactions among generative mechanisms and contextual conditions. (4) Theory and theorizing about causal processes are central to both scientific explanation and practical action. (5) Theory exists at multiple levels of abstraction, ranging from models to metatheory. (6) A diversity of methods can provide evidence in the search for causal processes operating in context. (7) As social scientists, we have an obligation to use social science knowledge to promote human flourishing. Although these insights may be familiar to many community psychologists who adopt an ecological approach to their work, we suggest that clearly articulating these principles can provide more solid foundations for inquiry in the field. We conclude the article by highlighting how critical realism may help to bridge the research-practice gap in community psychology and similar social sciences.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.077 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".