Bibliographic record
Abstract
Purpose: The main objective of this study was to explore the influence of environmental reporting on policy making. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings revealed that there exists a contextual and methodological gap relating to the influence of environmental reporting on policy making. Preliminary empirical review revealed the significant impact that environmental reporting, whether through traditional media or digital platforms, can have on the policy-making process. These studies have shown that the media's framing of environmental issues, its emphasis on urgency and consequences, and its ability to mobilize public opinion can shape the policy agenda, influence decision-makers, and lead to policy changes. Moreover, the role of environmental non-governmental organizations (NGOs) and social movements in leveraging media strategies to advocate for policy change cannot be understated. Environmental NGOs strategically use media campaigns to raise awareness, pressure policymakers, and drive environmental policy initiatives. Unique Contribution to Theory. Practice and Policy: The Agenda- Setting theory, Diffusion of Innovations theory and the Framing theory may be used to anchor future studies on environmental reporting. Based on the study, the following recommendations emerge: Foster closer collaboration between media and policymakers to enhance communication and understanding, invest in media training programs and establish reporting standards to improve the quality of environmental reporting, promote transparency and accountability in both media reporting and policymaking processes, encourage diverse and inclusive environmental reporting that represents all perspectives and communities, and embrace cross-platform and digital reporting strategies to enhance the reach and impact of environmental journalism in an evolving media landscape.
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 | 0.033 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".