Bibliographic record
Abstract
This review forum offers a critical assessment of Eric Sanderson's Mannahatta: A Natural History of New York City (2009), a work that employs cartographic and geovisualization techniques to reconstruct Manhattan Island as it would have appeared when Henry Hudson sailed into the harbour in 1609. The book, which accompanied an exhibit at the Museum of the City of New York, was published as part of the commemorative effort to celebrate the 400th anniversary of Hudson's voyage. For this reason, the visual representations and spatial narratives presented in Mannahatta raise significant questions about the role of geovisualization in the social construction of historical memory. The contributions to this forum represent a series of critical encounters with Sanderson's Mannahatta, focusing particularly on the cultural politics of landscape representation, the seductions of visual imagery, and the cartographies of utopian desire wrapped up in the imaginative geographies of Mannahatta. The authors acknowledge the achievements of Sanderson's decade-long research project. Yet, taken together, their contributions serve as a useful counterweight to the sensationalism that has surrounded the enthusiastic embrace of the Mannahatta Project in the pages of popular magazines, newspapers, and television documentaries. Such a critique is important because it has implications not merely for how we imagine the past but, more importantly, for how we envision the future in the perpetual present.
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 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 | 0.136 | 0.297 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.034 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".