The Political Economy of Digitalization and Climate Change Response in Nigeria
Bibliographic record
Abstract
Climate change is having serious impacts on the sustainability of the world. Growing scientific exactitude on causes and effects of climate change makes humanity’s response to it an urgent and critical need. Nigeria like many countries around the world is facing difficult times owing to the new environmental realities produced by the menace. Responding to climate change requires humanity’s best social, political, scientific, and technological efforts. To achieve this, this paper notes that digitalization can be a veritable climate change response tool in Nigeria. Dwelling on Schumpeter’s theory of innovation and the political economy approach, the paper argues that digitalization holds positives for Nigeria’s hitherto unimpressive climate change response but may not be practicable due to the social, economic and political contradictions that are producing unfavourable outcomes within the Nigerian state. This situation explains why mitigation and adaption efforts have not produced satisfactory results, a situation that puts citizens’ wellbeing in harm’s way in critical areas such as agriculture, housing, healthcare, and energy among others. The paper concludes that digitalization would make mitigation and adaptation wholesome and enhance the adequacy of climate change policies and programmes towards sustainable development in Nigeria. It then comes up with policy suggestions that can help mainstream digitalization as a tool for climate change response in the country.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".