The Rise of the Fourth Estate: The Media, Environmental Policy, and the Fight against Illegal Mining in Ghana
Bibliographic record
Abstract
Amid increasing climate change and environmental degradation, illegal mining (galamsey) has received widespread public concern in Ghana, with greater attention in recent times due to increased media coverage. This paper explores the role of the media in the ongoing crackdown on galamsey activities through a Media Agenda-setting theoretical lens. Media reportage on galamsey between January 2017 and March 2018 was analyzed using textual analysis. We observed that the media employed narratives and symbols to (re)shape public discourse and policy in the extractive industry. Stringent measures including Operation Vanguard and Galamsey Courts were established to fight the galamsey menace and the Multilateral Mining Integration Project was designed to provide alternative livelihoods following compelling media campaigns. The measures contributed to the seizure of equipment belonging to galamsey operators, arrest and prosecution of the galamsey operators, closure of illegal mines, land reclamation and programs to resettle the displaced galamsey operators. We discuss these findings in the context of the media's role in shaping the public opinion and policy agenda for environmental policy formulations and sustainable development.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".