Beyond “misery research”-- new opportunities for implementation research, policy and practice1
Bibliographic record
Abstract
Reflecting on a litany of failed expectations, dashed hopes and misjudged implementation processes, Swedish scholar Bo Rothstein dubbed the first quarter century of implementation research as “misery research, a pathology of social sciences.”2 The complex and multi-faceted nature of the policy implementation process now is a taken-for-granted among researchers, policymakers and practitioners. However, although complaints that “change is hard ” and “change takes time ” today produce rolled-eyed responses from policy analysts and policy makers alike, these responses were not anticipated in the early 1970s, when policy was expected to be more or less self-implementing, given necessary resources, regulation and resolve. This paper first takes a look at the domains of “misery research ” generally and in education in particular to draw lessons from the experiences of researchers, policymakers and practitioners during the 25 year period from approximately 1970-1995. It then looks beyond misery research to consider how the lessons of that period offer new opportunities to better understand implementation and the process of change in education, and how that understanding might lead to more effective education policies and practices.
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 | 0.337 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.099 |
| Scholarly communication | 0.035 | 0.088 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 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".