(Re)Thinking think tanks in the age of policy labs: The rise of knowledge‐based policy influence organisations
Bibliographic record
Abstract
Abstract The idea of ‘think tanks’ is one of the oldest in the policy sciences. Although the topic has been studied for decades, recent works dealing with advocacy groups, policy and behavioural insight labs and into the activities of think tanks themselves have led to discontent with the definitions used in the field, and especially with the way the term may obfuscate rather than clarify important distinctions between the different kinds of knowledge‐based policy influence organisations (KBPIO) operating in the contemporary policy landscape. In this paper, we examine the traditional and current definitions of think tanks utilised in the discipline and point out their weaknesses. We then develop a new framework to better capture the variation in the kinds of knowledge‐based organisations which operate in many sectors.
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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 | 0.048 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".