MétaCan
Menu
Back to cohort

The Role of Values in a Community-Based Conservation Initiative in Northern Ghana

2013· article· en· W2051433892 on OpenAlexaff
Lance W. Robinson, Kwame Ampadu Sasu

Bibliographic record

VenueEnvironmental Values · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsTabooWildlifeGovernment (linguistics)Variety (cybernetics)Community-based conservationResource (disambiguation)Wildlife conservationEnvironmental planningPolitical scienceEnvironmental resource managementBusinessPublic relationsEconomic growthGeographyEcologyEconomics

Abstract

fetched live from OpenAlex

In this paper we demonstrate the importance of non-economic values to community-based conservation by presenting findings from research into Kunlog Community Resource Management Area (CREMA) in northern Ghana. One of the central motivations for creating the CREMA was to reinforce a traditional taboo on bushbuck, and while some respondents mentioned the possibility of eventually attracting tourists, the primary desire behind the CREMA is to protect bushbuck and other wildlife for future generations. Several respondents emphasised wanting children and grandchildren to be able to grow up seeing the wildlife. Material benefits should not be the sole focus of those involved in promoting and legislating frameworks for community-based conservation – frameworks such as Ghana's CREMA policy. Government frameworks for the creation, registration and regulation of conservation initiatives should be flexible and able to accommodate diverse community-based conservation initiatives driven from a variety of mixes of motivations, including motivations deriving from non-material values.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.015
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.191
Teacher spread0.178 · 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 designQualitative
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

Citations13
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental ValuesSame topicConservation, Biodiversity, and Resource ManagementFrench-language works237,207