Lagoon Wanderings: Boat hydro-perspectivism in the aquapelagic assemblage of the Venetian Lagoon
Bibliographic record
Abstract
In this article I conduct the reader along a journey that follows different waterways within the Venetian lagoon and provides insights into the water-land interactions of this unique aquapelagic assemblage. Leveraging the methodological tool of hydro-perspectivism, I situate the analytical standpoint within two very different kinds of boat: the vaporetto (a local waterbus) and the kayak. The first, native to the lagoon, gives voice to a sample of the massive population of tourists that constantly crowds the lagoon’s islands and waters. Navigating the congested waterways leading from the Lido littoral to Venice’s historical centre, I raise issues such as over-tourism, water quality, and wave motion. Shifting the perspective to the kayak, a type of boat that originates in Inuit culture and is perfectly suited to the lagoon's shallow waters, the article then investigates the potentialities of analysing from the water’s edge, considering other serious problems of the Venetian aquapelago, such as pollution and hydro-morphological alterations. In conclusion, I argue that by conceiving the boat as not only as a means of transport but also as a means of acquiring and formulating knowledge, it is possible to revitalise hydrophilic feelings and thus the precious aquapelagic identity of Venice’s lagoon that has been in decline over the last century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".