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Record W4407800263 · doi:10.1007/s40152-025-00406-3

Theories of institutional change and marine privatisation

2025· article· en· W4407800263 on OpenAlexaff
Achim Schlüter, Kristof Van Assche, Sidy Fall, Khadidiatou Senghor, Hudu Banikoi, Elimane Abou Kane

Bibliographic record

VenueMAST. Maritime studies/Maritime studies · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of Alberta
FundersHORIZON EUROPE Excellent Science
KeywordsInstitutional changePolitical sciencePublic administration

Abstract

fetched live from OpenAlex

Abstract Privatisation, as a process that assigns more individual property rights, implies in most cases institutional change. Privatisation might occur on the level of society, when formal laws, but often also informal rules are changing, or it might take place on an organisational level when an asset under an open access regime, a cooperative, or a state-owned company is converted into a privately managed entity. From this perspective, it seems obvious that theories of institutional change provide a certain understanding of privatisation processes in the marine realm. Processes of marine privatisation are very heterogeneous in their characteristics: some processes are informal, some take part in the business world, others in the political realm, some are to a certain degree planned, others are emerging and have more evolutionary characteristics, some are characterised by huge power asymmetries others take place under more equal footing. Therefore, this paper interrogates a broad range of theories of institutional change. Our perspective does not proclaim or investigate superiority of one theory above the other, but rather inquires about fit . After elaborating on the theories, clarifying their focus, core concepts and assumptions, the paper illustrates the explanatory powers of the theories by looking at the case of privatisation of space in Saint Louis, Senegal. Due to strong restrictions for Senegalese fishers to fish in Mauritanian waters, the establishment of a marine protected area, and more recently the establishment of a gas field on the doorstep, fishers are confronted with an enclosure of their commons.

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 categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.011
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 head0.030
GPT teacher head0.274
Teacher spread0.244 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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