Press Coverage of Climate Change Issues in Nigeria and Implications for Public Participation Opportunities
Bibliographic record
Abstract
Nigeria faces a lot of environmental problems such as extensive gas flaring, deforestation, and desertification with serious consequences on climate change. How are these issues covered and framed by Nigerian newspapers? Content analysis of systematically sampled, 438 issues from 4380 issues of four purposively selected dailies between 2007 and 2009 shows dominance of climate politics/economics issues (61.2%), foreign sourcing of reports (63.4%), straight news formatting of reports (83.6%) and framing in terms of mitigation (55.2%). Mitigation efforts aim to reduce or prevent emission of greenhouse gases implicated in climate change. We conclude that coverage and framing constrain opportunities for popular participation in climate change discourse. To improve the situation, Nigerian newspapers should broaden the scope of climate change coverage and framing, widen local sourcing of reports, diversify the formats of reporting, and frame the issues more in the mould of adaptation (activities and measures to reduce risks posed by climatic changes) to boost involvement of people in climate change discourse through monitorial, supportive and collaborative strategy in agenda setting agenda.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".