Climate Change – Probable Socio-Economic Systems (SES) Implications And Impacts In The Anthropocene Epoch
Bibliographic record
Abstract
Abstract It is vital for security experts to learn from the historical records of global climate change as to how the human society has been impacted by its consequences in the “new” Anthropocene Epoch. Some of these consequences of global climate change include the perishing of several human settlements in different parts of the globe at different times, e.g., in 1700 B.C., prolonged drought contributed to the demise of Harappan civilization in northwest India. In 1200 B.C., under a similar climatic extremity, the Mycenaean civilization in present-day Greece (as well as the Mill Creek culture of the northwestern part of the present-day US state of Iowa) perished. Why did some societies under such climatic events perish while others survived? Lack of preparedness of one society and its failure to anticipate and adapt to the extreme climatic events might have attributed to their extinction. The authors will also analyze the extinction of one European Norse society in Greenland during the Little Ice Age (about 600 years ago), as compared to the still-surviving Inuit society in the northern segment of Greenland, which faced even harsher climatic conditions during the Little Ice Age than the extinct Norsemen. This is how the adaptability and “expectation of the worst” matter for the survival of a particular community against climatic “black swan” events (Taleb, 2007). Similar impacts in terms of sea-level rise expected by the year 2100 whereby major human populations of many parts of the world are expected to lose their environmental evolutionary “niche” will be discussed. Rising temperature will not only complicate human health issues, but also will it take its toll on the staple food producing agricultural belts in some latitudinal expanse. It will also worsen the living condition of the populace living in areas where climate is marginal. Through the Socio-Economic Systems Model provided by Vadineanu (2001), the authors will next consider the effect of extant policy-making “prisms” responding to climate change (such as the “Club of Rome” versus the “Club for Growth” visions) as concerns the ongoing process of globalization and survival of the nation-state.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".