Bibliographic record
Abstract
It's impossible not to think, even upon close inspection, that Olivo Barbieri's photographs aren't images of obsessively detailed architectural maquettes. The trees seem plastic, the cars resemble toys and the buildings look as though they would fall over if you so much as breathed on them. The Waterfall Project brings this unreal quality to landscape, specifically to such touristy waterfalls as Victoria (Zambia/ Zimbabwe), Iguazu (Argentina, Brazil), Khone Papeng (Laos/Cambodia) and Niagara (USA/Canada). In these disorienting images, the spectators on the crowded viewing platforms look like M & M's in a candy bowl, a cluster of toytown Pointillistic color against a backdrop of watery froth. The results are vertiginous and wonderfully bizarre. Critic Walter Guadagnini writes in the introduction: There is an evident technical expedient in this, and it is the choice to photograph from above, to place oneself in a privileged and anomalous condition. In the past, this expedient already gave rise to numerous readings, which range from acknowledging the historical roots of this perspective (going back all the way to Nadar's photographs from a hot-air balloon) up to the socio-political implications deriving from 9/11.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.180 | 0.035 |
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".