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Record W2612587943 · doi:10.1080/14649365.2017.1323342

Police power and fettered freedom: regulating coastal freedom camping in New Zealand

2017· article· en· W2612587943 on OpenAlexaff
Damian Collins, Robin Kearns, Laura Bates, Elliott Serjeant

Bibliographic record

VenueSocial & Cultural Geography · 2017
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Alberta
FundersUniversity of Auckland
KeywordsEnforcementUnderpinningPower (physics)Space (punctuation)SociologyPublic spaceLaw enforcementLawPolitical scienceCivil engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Freedom camping is a form of tourism entailing overnight stays in public open spaces, rather than formal campgrounds. It presents varied challenges for local governments charged with maintaining safe and orderly public spaces. This article provides empirical and conceptual insights into the regulation of coastal freedom camping in New Zealand, drawing on the notion of police power. This form of law is centrally concerned with preventing disruption and disorder in public space, and seeks to advance collective welfare rather than individual rights. The purpose of this article is twofold. First, we consider why and how local governments in New Zealand regulate coastal freedom camping, focusing on a case study of the Coromandel district. Second, we consider how freedom campers understand and experience the regulation of their activities, drawing on a survey of 61 campers in three North Island coastal areas. We find that the policing of freedom camping proceeds through regulation of space, objects and behaviours. Underpinning this approach is an understanding of the activity as inherently problematic. Freedom campers themselves were generally aware of local regulations, but had little experience of enforcement. Most sought to perform camping responsibly, whilst noting that some others required policing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.323
Teacher spread0.297 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations16
Published2017
Admission routes1
Has abstractyes

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