The human dimension of climate change research in Greenland: Towards a new form of knowledge generation
Bibliographic record
Abstract
In the field of climate change research, social sciences have lagged behind natural sciences and have not yet mustered enough recognition from the public. Studies on the human dimension of climate change commonly use the concepts of ‘vulnerability’ and ‘resilience’. The ‘resilience’ approach investigates the capacity of a community that absorbs environmental disturbances, so as to retain essential social, cultural, and economic structures, while the ‘vulnerability’ approach seeks to identify factors that make the community in question vulnerable to ongoing or future climate change. The term ‘resilience’ tends to give an impression that a system may remain static, and because of this, I adopt the term ‘vulnerability’ in this essay. ‘Vulnerability’ does not mean that Arctic communities are always “vulnerable” to environmental changes but may be negatively impacted by the associated social and political changes. Accordingly, vulnerability means the social and political “characteristics” of the community that is experiencing the changes. This concept helps researchers direct their attention not only to environmental changes, but also to the societal situation of the community. In the second half of this essay, I exemplify how the vulnerability approach works, drawing data from my fieldwork conducted in Siorapaluk, in 2009. More local communities want scientific data in order to plan a course of action and to shape their political and economic policies in the rapidly changing environment. In future, it will be increasingly important for natural scientists to work closely with local communities, and this may lead to a new form of knowledge generation.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".