Bibliographic record
Abstract
Research has shown that most developing nations like Nigeria are increasingly confronting numerous environmental problems such as greenhouse gases, oil spills, and toxic waste management. However, the real problem has to do with peoples' negative attitudes and behaviors to the environment and resultant environmental problems. For instance, a high percentage of wastes generated in most developing countries in Africa are currently being disposed of via open dumping - which refers to a situation where people dump refuse on the streets. As responsible citizens, however, we should care more for the environment. We must take steps to reduce our personal ecological footprints, educate ourselves on what engenders environmental problems as well as change our attitudes towards our environment. In the last decade, computer-human interaction (CHI) research has evolved in response to the considerable increase in peoples' understanding of the influential roles that technology plays in everyday life. Technologies that are essentially designed for the purpose of influencing users to change their behaviors and attitudes, without using coercion or deception is referred to as Persuasive technology (PT). These behavioral and attitudinal changes are realized via the use of persuasive strategies. Persuasive strategies are techniques that are used in PT design to motivate behavior change and influence people to achieve specific goals in various domains like ecommerce, health and even in environmental management. As a first step towards contributing to research, this study focuses on how to design and implement a mobile persuasive system (a behavioral change support system BCSS) for waste management. This system when implemented could motivate users to change their attitudes and behaviors towards waste disposal and to care more for the environment.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.014 |
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".