Exploring the Eco-Psychological Impact of Hyper-Technologized Environments and Ecological Destruction in Anil Menon’s The Beast with Nine Billion Feet
Bibliographic record
Abstract
This paper delves into the intricate relationship between human beings and Earth's ecosystems, emphasising their mutualistic reliance on survival. However, contemporary challenges, characterised by excessive resource exploitation and the rise of hyper technologized environments, have resulted in a significant disconnect between humans and nature. This disconnection has precipitated various psychological issues and severed vital bonds between individuals, obstructing adherence to fundamental humanistic principles. Focusing on Anil Menon's book, The Beast with Nine Billion Feet, this paper operates within the framework of eco-psychology, utilising Richard Louv's concept of Nature Deficit Disorder to investigate the widening chasm between humans and nature. Ethical dilemmas, especially concerning the impact of AI and synthetic life forms, have also been explored in this context. The research objective was to unravel the unintended eco-psychological consequences resulting from excessive resource exploitation and an overemphasis on artificial environments. Employing a narrative method, this paper analyses plots, characters, and situations to illustrate this eco-psychological crisis. The main findings underscore Menon's portrayal of characters, showcasing innate human instinct to forge a profound connection with nature. Those residing in proximity to nature lead fulfilling lives, whereas individuals isolated from the environment exhibit varying degrees of Nature Deficit Disorder, compelling them to seek solace in nature.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".