Community knowledge towards electric vehicles and policy part II: A pilot study of Edmonton Height underserved neighborhood in Huntsville, Alabama
Bibliographic record
Abstract
Introduction: Electric Vehicles (EV) are fast emerging globally as a viable alternative to traditional fossil fuel burning cars and are now being presented as a resolution for the problem of dependence of fossil fuels, increasing emissions, and other environmental issues. Purpose and Objective: The study explores the neighborhood knowledge toward green mobility and the objective of this paper is to investigate and examine neighborhood perceptions and understand their knowledge towards the electric vehicle. The research paper goal necessitated the knowledge of the underserved community towards green mobility. Methods: Following the literature review research phase, the researcher conducted several semi structured interviews with underserved community. To best augment the quantitative, data were gathered from underserved Edmonton Height community, through the design of questionnaire survey. Data collection took place during the last two weeks of October 2018. Neighborhood households were approached during the day and evening in their residents using a structured questionnaire. Results: The analysis reveals that that 60% of the respondents not aware of plug-in EV incentives (such as tax credit, rebate, high occupancy lane access, reduced tolls, lower vehicle registration rates, or discounted electricity rates) offered by the federal government; their state government; local community; their electricity provider; their employer, while 10% indicated federal government and 10% local community and 5% indicated electric providers, 5% employers and 5% state government. However, the pilot results are a useful estimate of the number of households residents residing in Edmonton Heights don’t know that plug-in EVs can be recharged from a regular home outlet. Conclusion: The paper concludes that the progress that the electric vehicle industry has seen in recent years is not only extremely welcomed, but highly necessary considering the increasing global greenhouse gas levels and it should be noted that a range of technology options is being aggressively explored to facilitate the transition to a more sustainable transport system. Near term, technologies such as EVs can provide sustainable mobility and help alleviate some of the problems created by conventional vehicle powered by fossil fuels. Notwithstanding, the pilot results are a useful estimate of the number of households residents residing in Edmonton Heights don’t know that plug-in EVs can be recharged from a regular home outlet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".