Does Green Technology Espousal Really Matter?
Bibliographic record
Abstract
There is immense pressure across the world to adopt green technology and green practices in order to reduce the pollution level of urban and cosmopolitan areas. While, the adoption of green technology depends on several social and relative factors, there was so reluctance on the part of many to adopt it. Contextualizing the area of concern to the Tuk-Tuk, drivers in the city of Bangkok, Thailand, a study was conducted to assess the factors influencing adoption of green technology. The study followed qualitative research initially to explore unique factors and then confirmed those factors through qualitative research. Factor analysis was conducted to analyze the reliability of the instruments and data collected from the “tuk tuk” drivers. Almost 176 auto drivers were interviewed (informally) with a structured schedule and data collected. To analyze this data, study followed statistical tools like correlation and regression. The results clearly indicate that socioeconomic factors which are moderated by the green technology factors that influence the “TukTuk” drivers intention ‘not to go green’, even though they have the keen interest towards environmental concerns.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".