“Resilient individuals; resilient societies”: the role of geographical education in their development
Bibliographic record
Abstract
This paper examines how resilience is defined and understood in the context of geography education, and how geography curricula can contribute to developing resilience in individuals and societies. Although resilience has been studied widely in fields like ecology and psychology, its role in education needs further attention. This study distinguishes between narrow, individualistic conceptions of resilience focused on personal coping skills, versus broader definitions encompassing the capacity of communities and societies to anticipate, respond to, and transform in the face of disruptions. An analysis of secondary school geography curricula from Australia, Canada, China, Ethiopia, South Africa, and the United States reveals that while the term “resilience” itself is rarely mentioned explicitly, the curricula incorporate many related concepts such as risk, adaptation, sustainability, and human-environment systems. Specific topics that lend themselves to developing resilience understanding include natural hazards, climate change, resource management, and human migration. Coupling pedagogical approaches like problem-based learning, GIS mapping, and research projects also lend themselves to building resilience capacities. We argue that by developing spatial thinking abilities and understanding of interconnected environmental and societal systems, geography education holds unique potential to cultivate resilience. Now is the time to explicitly incorporate resilience in geography curricula to ensure this potential is realized. Enhancing resilience capacities through geography education is vital for empowering current and future generations to navigate an unpredictable world.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".