The Politics of Evidence-Based Policy Making
Abstract
“Evidence-based policy making” (EBPM) has become a popular term to describe the need for more scientific and less ideological policy making. Some compare it to “evidence-based medicine,” which describes moves to produce evidence, using commonly-held scientific principles regarding a hierarchy of evidence, which can directly inform practice. Policy making is different: there is less agreement on what counts as good evidence, and more things to consider when responding to evidence. Our awareness of these differences between science and policy are not new. Current debates resemble a postwar policy science agenda, to produce more scientific and “rational” policy analysis, which faced major empirical and normative obstacles: the world is not that simple, and an overly technocratic approach to policy undermines much-needed political debate. To understand modern discussions of EBPM, key insights from previous discussions must be considered: policy making is both “rational” and “irrational”; it takes place in complex policy environments or systems, whose properties should be understood in some depth; and it can and should not be driven by “the evidence” alone.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
LL 144 requires transparent auditing so job seekers are empowered to make informed choices and employers and vendors are incentivized to deploy fair(er) models, but it does not set a floor for permissible rates of discrimination.
“LL 144 requires transparent auditing so job seekers are empowered to make informed choices and employers and vendors are incentivized to deploy fair(er) models, but it does not set a floor for permissible rates of discrimination.”
From Null Compliance: NYC Local Law 144 and the challenges of algorithm accountability (2024) · cites this paper as [5]
Cited by
Sorted by citations
The dark side of coproduction: do the costs outweigh the benefits for health research?
Oliver, K., Kothari, A., & Mays, N. (2019) · Health Research Policy and Systems · doi:10.1186/s12961-019-0432-3 · Cited by 754 · see citing works
Parkhurst, J. (2016) · London School of Economics and Political Science Research Online (London School of Economics and Political Science) · doi:10.4324/9781315675008 · Cited by 214 · see citing works
The UK Government’s COVID-19 Policy: What Does “Guided by the Science” Mean in Practice?
Cairney, P. (2021) · Frontiers in Political Science · doi:10.3389/fpos.2021.624068 · Cited by 79 · see citing works
Self-citationNull Compliance: NYC Local Law 144 and the challenges of algorithm accountability
Wright, L., Muenster, R. M., Vecchione, B., Qu, T., Cai, P. (., Smith, A., Comm 2450 Student Investigators, Metcalf, J., & Matias, J. N. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658998 · Cited by 43 · see citing works
- Authors
- Cairney, P.
- Affiliations
- University of Stirling (GB)
- Corresponding author
- Cairney, Paul
- ORCID
- Cairney, Paul
- Venue
- Oxford Research Encyclopedia of Politics
- Publisher
- Oxford University PressNew York, NY
- Published
- 2017-05-24
- OpenAlex
- W4300749320
- Cited by
- 625
- References
- 0
- Citations by year
- 2026: 3 · 2025: 39 · 2024: 75 · 2023: 61 · 2022: 107 · 2021: 101 · 2020: 76 · 2019: 84 · 2018: 50 · 2017: 19 · 2016: 8 · 2014: 1 · 2012: 1
- Type
- reference entry
- Language
- en
- Open access
- closed
- Locations
- Oxford Research Encyclopedia of Politics (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
- Social Sciences › Public Administration › Public Policy and Administration Research
- Social Sciences › General Social Sciences › Social Science and Policy Research
- Keywords
- evidence-based policy making, science-policy interface, evidence hierarchy, policy analysis, technocracy, policy complexity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review