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Record W2040494053 · doi:10.3152/146155107x210917

Contextual phases in the institutionalization of the environmental assessment of road development in Cameroon

2007· article· en· W2040494053 on OpenAlexaff
Dieudonné Bitondo, Pierre André

Bibliographic record

VenueImpact Assessment and Project Appraisal · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsInstitutionalisationContext (archaeology)SummitPolitical scienceEnvironmental planningSustainable developmentPovertyCorporate governanceProcess (computing)Economic growthEnvironmental resource managementPublic administrationBusinessGeographyEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

The aim of this paper is to demonstrate that the institutionalization of the environmental assessment (EA) of road development in Cameroon is context-sensitive. A content analysis of literature and key stakeholders' interviews reveals that socio-economic processes of the 1990s guided by, among others, the concepts of sustainable development, poverty reduction and good governance shaped the context of forestry and environmental policy reforms that led to the institutionalization of EA of road development. The Rio Earth Summit, the involvement of donors and non-governmental organizations in release mechanism programmes and projects enhanced this process. With this background, three phases were determined: the marginality phase (before 1992), when roads were constructed without EA, a general formalization phase (1992–1994), during which a nationwide EA framework was promulgated, and a specialization phase (after 1996), when the implementation of EA of road development became operational, even before the nationwide system. An understanding of the context must be included in the institutionalization process of EA, especially in developing countries.

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)0.001
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 score0.021
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.392
Teacher spread0.368 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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