Community Psychology and Transformative Policy Change in the Neo‐liberal Era
Bibliographic record
Abstract
I present ideas about how community psychologists, as researcher-activists, can influence public policy. I begin by describing the current neo-liberal era, noting the immense obstacles it poses to progressive policy change. Next I contrast two approaches to understanding policy formation, evidence-based policy and discursive policy analysis, and argue that transformative policy change can benefit from both approaches. I then propose three types of policy outcomes that community psychology research and activism should aim to promote: (a) shaping problem definition, (b) controlling channels for debate and participation, and (c) allocating resources. I use examples from community psychologists' involvement in policy, mostly in Canada, to illustrate how such policy change can be both achieved and constrained. I conclude by discussing implications for theory and practice related to policy change.
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 | 0.048 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.133 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".