Bibliographic record
Abstract
Abstract | Jessica Auer’s photographs often depict the impact of tourism on some of the world’s most popular places, showing how landscape has been preserved, altered, or commodified for sightseeing. Her series On How to View Landscape proposes that we also project our own mythologies and perceptions onto nature. In this project, we see the landscape in relation to ourselves, the view to a degree still a backdrop onto which we project our curiosities and desires. Upon leaving these viewpoints we may or may not leave a trace, but we somehow become part of the overall image.Résumé | Les photos de Jessica Auer montrent souvent l’impact du tourisme sur certains des lieux les plus populaires au monde, en particulier la manière dont les paysages sont préservés, modifiés ou transformés par le tourisme. Sa série Comment observer un paysage propose que nous projetions nos propres mythes et perceptions sur la nature. Dans ce projet, nous voyons le paysage en relation avec nous-mêmes, la vue un arrière-plan sur lequel nous projetons notre curiosité et nos désirs. Après avoir quitté ces lieux nous laisserons peut-être une trace mais d’une certaine manière nous faisons partie de l’image dans son ensemble.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.012 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".