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Record W2415223578 · doi:10.1080/21550085.2016.1173791

Citizens, Leaders and the Common Good in a world of Necessity and Scarcity: Machiavelli’s Lessons for Community-Based Natural Resource Management

2016· article· en· W2415223578 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEthics Policy & Environment · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransparency (behavior)Natural resource managementScarcityNatural resourcePolitical scienceSociologyProcess (computing)Value (mathematics)Work (physics)Economic JusticeTheme (computing)Adaptation (eye)Public relationsComputer scienceLawEconomicsPsychologyEngineering

Abstract

fetched live from OpenAlex

In this article we investigate the value and utility of Machiavelli’s work for Community-Based Natural Resource Management (CBNRM). We made a selection of five topics derived from literature on NRM and CBNRM: (1) Law and Policy, (2) Justice, (3) Participation, (4) Transparency, and (5) Leadership and management. We use Machiavelli’s work to analyze these topics and embed the results in a narrative intended to lead into the final conclusions, where the overarching theme of natural resource management for the common good is considered. Machiavelli’s focus on practical realities produces new, sometimes unsettling, insights. We conclude that this focus helps to understand the development and performance of management regimes and their consequences and that institutional design should be seen as an ongoing process, which requires a constant adaptation of these institutions.

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.

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)0.003
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.091
GPT teacher head0.353
Teacher spread0.263 · 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