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Record W2153380382 · doi:10.5539/jsd.v6n5p1

Can Print Media Discourse Drive Forest Policy Change in Bangladesh?

2013· article· en· W2153380382 on OpenAlexvenueno aff
Md. Nazmus Sadath, Max Krott

Bibliographic record

VenueJournal of Sustainable Development · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Media policyContent analysisPrint mediaDiscourse analysisEnvironmental policyQualitative researchPolitical scienceSociologyGeographySocial scienceEnvironmental planningPoliticsMedia studies

Abstract

fetched live from OpenAlex

Forest issues are widely discussed in Bangladeshi print media. Different policy actors (both central and peripheral) participate in the media discussion to express their opinions on and interests in forest issues. The media play an important role in the construction of the social, environmental and economic context of the Bangladeshi forest sector where forest policies are formulated and modified. Nevertheless, while extensive research has been carried out to explain the formation of the environmental discourse, very little research has been done on the specific relationship between the policy outcomes and media discourses. This study tries to determine the influence of media discourse on forest policy changes in Bangladesh. It analyses the media discourse from 1989 to 2009 in the “The Daily Ittefaq”, a reputed Bangladeshi print medium of Bangladesh, along with Bangladeshi forest policy documents from 1989 to 2010. A quantitative, qualitative content analysis, followed by expert interviews of forest policy decision makers was the chosen methodology. The empirical findings of the study reveal that media discourses do not drive the forest policy change in Bangladesh; rather the international concurrent forest discourses trigger symbolic forest policy changes in Bangladesh.

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 score0.033
Threshold uncertainty score0.755

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)0.0010.000

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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

Citations13
Published2013
Admission routes1
Has abstractyes

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