Bringing Governments Back in: Governance and Governing in Comparative Policy Analysis
Bibliographic record
Abstract
In many visions of governance, governments are portrayed as playing a “steering”, rather than “rowing”, role. The widespread use of privatization, deregulation, decentralization and third-party governments are often mentioned as concrete manifestations of the broad transformation which has led to new forms of governance. Examined more closely, however, the large and growing body of literature on governance has done little to clarify what is “new” about “new governance”. Does it indicate a clean break from institutions and processes of the past, or is it merely chronicling an assortment of instrument changes necessary for governments to adapt to changing socio-economic conditions? Do the changes really indicate the emergence of a new system in which the government is merely another player on a par with societal and international counterparts? More fundamentally, is governance a normative framework reflecting the hopes and desires of those who prefer smaller governments, or an empirical description of an existing reality? This article briefly surveys existing studies in the field as an introduction to the articles in this special issue. These articles provide strong arguments in support of the view that governments continue to play a pivotal role in policy-making, and that if this fact is not taken into consideration then the perception is of governance risk being anchored to a merely normative or prescriptive view rather than an empirically robust one.
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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 | 0.032 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".