Responsive to What? Explaining the Information Quality of Public Comments on Bureaucratic Policymaking Using a Text‐as‐Data Approach

Adriana Bunea et al.

Governance2026https://doi.org/10.1111/gove.70122article
AJG 4
Weight
0.50

What the paper says

What drives the quality of information that public comments on bureaucratic policymaking provide? We address this question along a text‐as‐data approach, develop and validate four measures capturing the multidimensional concept of information quality, and apply it to an original dataset covering more than 20,000 public comments across 1036 policy acts issued by the European Commission between 2016 and 2021. We argue that the interplay of information demand and supply and institutional factors matter in particular for varying information quality of public comments. Our results show only mixed evidence regarding the claim that high‐quality comments are more likely during the policy formulation stage. Counterintuitively, we find that more informationally dense and syntactically complex EC policy documents generate public comments of higher quality. Regarding policy areas, information quality is unrelated to the age of European policy while more technical fields receive public comments of lower quality on average. Based on developing and validating an innovative empirical strategy, we thus provide novel insights into how the design of public commenting procedures and crafting of policy acts shape the information quality provided through public consultations.

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https://doi.org/https://doi.org/10.1111/gove.70122

Or copy a formatted citation

@article{adriana2026,
  title        = {{Responsive to What? Explaining the Information Quality of Public Comments on Bureaucratic Policymaking Using a Text‐as‐Data Approach}},
  author       = {Adriana Bunea et al.},
  journal      = {Governance},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/gove.70122},
}

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Responsive to What? Explaining the Information Quality of Public Comments on Bureaucratic Policymaking Using a Text‐as‐Data Approach

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.