Beauty and Threat: The Effect of the Icelandic Landscape on the Works of Icelandic Landscape Painters
Bibliographic record
Abstract
This research paper explores the subject of topophilia – a strong sense of place – as it relates to the effects of geography, environment and social influences on the works of Icelandic painters. Regarding Iceland specifically, I was interested in how the striking duality of beauty and threat affects the artist’s psyche and vision as it relates to a painted landscape. Certainly, artists world-wide are inspired by their surroundings, either beautiful or terrible, but few people have a more intimate relationship with their surroundings than Icelanders. Few countries are as homogenic as Iceland – most of the population is related to one another within eight generations and most are distantly related to the handful of settlers who arrived in 874 AD. Historically, Iceland has been socially and geographically cut off from the rest of the world, viewed as little more than a tax base by Denmark until WWII, followed by its subsequence independence in 1944. Because of its isolation, Iceland’s history of fine art is a short one – less than 200 years. These factors, along with its history of volcanic activity make Iceland a compelling study. My purpose was to explore how experience, place, geography and landscape drill into peoples’ psyches and percolates, to later emerge from the subconscious as imagery. It is my hypothesis that deep connections to this strange and beautiful landscape – this juxtaposition between beauty and threat – would emerge and that it would relate to the fine art that Icelandic artists produce. Faculty Mentor: Annetta Latham Department: Arts and Cultural Management
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".