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Record W4398660920 · doi:10.7910/dvn/h7okej

Losing control is not an option

2023· dataset· en· W4398660920 on OpenAlexaboutno aff
Sebastian Roché, Simon Varaine

Bibliographic record

VenueHarvard Dataverse · 2023
Typedataset
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsReplication (statistics)Resource allocationControl (management)Resource (disambiguation)BusinessComputer securityComputer scienceOperations researchPolitical scienceEngineeringMedicineComputer network

Abstract

fetched live from OpenAlex

Here are the main indicators used in the paper "Losing control is not an option. Resource allocation to police oversight agencies in Western states". Abstract: Independent police oversight is a specific government delegated function that has been neglected by scholars of regulation. The main goal of this article is to understand the allocation of state resources to independent police oversight agencies (POAs) in the post delegation stage. We test whether the aim of delegation is better governance in complex areas to increase police agents' accountability (“policy complexity”) or to avoid political costs of agencification (“agency losses”). A survey of 27 POAs in Europe and Canada shows that POAs tend to receive significantly fewer state resources when they have a high level of formal independence or strong legal empowerment. Resource allocation seems more congruent with an “agency losses” logic than with the goal of making regulation more efficient. Our findings have notable implications for international norm-setting bodies (the UN, the Council of Europe), who have not sufficiently codified the allocation of resources.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0210.004

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.

Opus teacher head0.041
GPT teacher head0.235
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueHarvard DataverseSame topicLaw, Economics, and Judicial SystemsFrench-language works237,207