1 Ineffective policies: causes and consequences of bad policy decisions
Bibliographic record
Abstract
This volume is concerned with policy that is developed, adopted, and/ or sustained despite being shown as ineffective/ ineffectual and generating undesirable and/ or unanticipated negative outcomes.Such policies are by their very nature ineffective.This definition grounds research in empirical cases of how and why governments develop and pursue ineffective policy.Such a position draws from the American political scientist Harold Lasswell, who helped found the field of policy studies.He argued that policy work ought to be multidisciplinary, focused on problem solving, and explicitly normative (Torgerson, 1985).Taking their cue from these principles, contributors to this book sought to identify ineffective policies and to lay the foundations for citizens, scholars, and policy makers alike to tackle them in more comprehensive ways that account for government deficiencies without seeking to undermine their work.Hailing from a broad range of disciplinary backgrounds and methodological approaches, contributors to this book share a commitment to improving trust in governments and to the development and implementation of the policies necessary to tackle problems in and across the diverse fields of environment, finance, and technology.Part of this framing is an acceptance that research on ineffective policy requires an explicitly normative approach that recognizes there are good and desirable policies, as well as bad and undesirable policies.Not shying away from using such words allow scholars and policy makers alike possibilities to consider the substantive questions openly and honestly about the type of policies that societies need and ought to favor, as well as those that governments ought not to adopt and implement.Being explicitly normative in identifying and advancing solutions to serious societal problems has its place in policy studies.Doing so in no way removes the rigor and systematic research upon which the policy assessments and recommendations provided in this volume rely.All research and policy analyses are subject to biases which we argue need to be explicitly acknowledged.Identifying ineffective policies as not meeting outcomes, failing to produce expected outputs, having implementation issues, generating unexpected effects, and/ or having outright failed all ultimately requires judgment underlined by normative stances.Referencing effective policy as good and desirable, and ineffective policy as bad and undesirable, in short, helps engage in more foundational debates while maintaining a focus on much-needed policy improvement.It also allows the connection with language used in everyday framing of policy, opening important spaces for communication beyond the ivory tower/ academic world.Ineffective policies in Europe and North America are identified in this book across three fields that are core to societal well-being in the 21st century.These policy fields have been selected, in fact, because of their significance for the future well-being of populations around the globe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".