A Tiny Comment to Utilizing Religious Sources to Create Environment Friendly Citizens
Bibliographic record
Abstract
The world's assets are lessening and nature is in retreat. In less than a century, human populace and its needs for space, materials, merchandise, and conveniences have enlarged more than four times. Some of the environment spoiling problems are to remember: the over-cut of woods, depleting of wetlands, spread of horticultural advancement and high rates of pesticide and compost use, the spread of wild nuisances, unplanned clearing of forest for household stock, urbanization of foreshores, and the contamination of streams and estuaries. All these natural harms are caused because of thoughtless human works. Knowing these, issues about environmental cleaning, protecting the environment, to prevent damage to the ecological balance and such issues have been being mentioned and emphasized too often in media outlets in the last two decades. In order to implement these highlighted issues in society, foundations and associations are formed. These organizations foster to set up various activities to intensify people’s attention to the subject and create environmental awareness among public. Herewith, so many dynamics can be utilized to motivate people to be well and responsible in life. Among the dynamics, one of the most important factors that motivates people at ease is religion. Benefiting from religious motivation or sources while changing people's habits or perceptions can be a good solution in shaping conscious citizens. Moreover, to make people more sensitive about these issues and more addicted to their responsibilities, religions’ effect is inevitable. In this article, the researcher points out that with the aid of diversity and complexity of religious sources, better environment friendly citizens may appear in the society.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.029 | 0.034 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".