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Record W2156265590 · doi:10.18352/ijc.548

A study of institutional origins and change in a Canadian urban commons

2015· article· en· W2156265590 on OpenAlexafffundabout
James P. Robson, A. John Sinclair, Alan P. Diduck

Bibliographic record

VenueInternational Journal of the Commons · 2015
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrassrootsCommonsCommon-pool resourceCorporate governanceHarmony (color)Resource (disambiguation)Public relationsSociologyPolitical scienceEnvironmental resource managementPublic administrationBusinessLawEcologyEconomics

Abstract

fetched live from OpenAlex

Kenora is a small city located in northwestern Ontario, Canada. The study presented here focuses on Tunnel Island, 300 acres of forested land adjacent to Kenora’s downtown. The island is used and valued by both city residents and members of three nearby Ojibway nations. As a multiple-use, common-pool resource accessed by different groups for a range of non-extractive activities, the site has become an experiment in multicultural commons governance, and presents an excellent opportunity to examine the origins and development of institutions for managing collective environmental resources in an urban setting. Using participant observation, internet- and field-based user surveys, and semi-structured interviews, our research finds that grassroots ‘governance’ of the site is emerging through subtle processes of individual and social construction, with the strategies and norms (codes of conduct) employed by users providing relative harmony on the trails, which suggests functioning commons institutions. Nevertheless, values-based and epistemic tensions exist among users, pointing to governance challenges for planned joint management of the site, and specifically the need to develop formal, legitimate, and yet flexible and inclusive arrangements that can operate in conjunction with the social practice of existing users.

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 score0.186
Threshold uncertainty score0.972

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 studies0.0000.000
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 head0.050
GPT teacher head0.274
Teacher spread0.223 · 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

Citations1
Published2015
Admission routes3
Has abstractyes

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