Shifting formal education toward hydrosocial and hydrorelational learning
Bibliographic record
Abstract
The way that freshwater is accessed, used, consumed, and managed in highly industrialized countries demonstrates our lack of value or respect of water.Consider the adage, "show me your budget and I'll tell you your priorities" and put it in terms of freshwater: show me how you use water and I'll tell you how you value it.Show me where and how freshwater is incorporated into your formal education curriculum, and I'll show you how your society respects freshwater.Understanding and managing water resources is a social responsibility and activity.However, many societies view water as political power, an economic tool, an engineering exercise.Most peer-reviewed research speaks to water from a scientific perspective, placing humans either outside the problem or as the primary beneficiaries or deterrents of the outcomes [1].We argue that water research and understandings come from interconnections and should be analyzed through the lens of relationships rather than in selected isolation.Interpretations of data must include a holistic approach, incorporating humans, rather than having humans viewed as a consumer or manipulator of nature and natural resources.The necessity of bringing to bear the benefits of multidisciplinary critical thinking and problem solving is clear in terms of identifying meaningful and successful approaches to informing societies about the intersecting and interconnected complexities of changing climate, extreme weather events like floods and droughts, and equitable water use and resource management.However, Western approaches to water are human-centric, resource-based approaches that do not consider or forefront ecological needs, functions, and benefits.At a minimum, Western cultures must shift language in science and educational systems from the perfunctory hydrologic cycle (scientific) or hydrosocial (human-centric) view of water to an interconnectedness model described as either a broader hydrorelational approach to valuing freshwater resources requires that formal water science pedagogies are fully integrated with social science and humanities education [1-3].Both hydrologic and hydrosocial frameworks-and distinctions between the two-have become relatively common in the scientific and social sciences literature [4][5][6][7].The hydrologic framework is well established and accepted for both formal education and scientific purposes.It's familiar and comfortable, placing water as-independent from and of social systems.It's readily understandable, focusing on scientific and geographical facts about water, including rote presentations of chemical properties, meteorological processes, physical state changes, and the existence of groundwater.Hydrosocial perspectives enable us to move toward viewing our perceptions and relationships with water as emergent from social-ecological frameworks [1,8].In a hydrosocial
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".