From Land Grab to LandBack · LandBack Landscape Poems
Bibliographic record
Abstract
Part Rematriation manifesto, part anti-colonial propaganda pamphlet, part anti-imperialist exhibition catalog, From Land Grab to LandBack / LandBack Landscape Poems brings in print format calls, strategies, and prose against the ecological and social spoliation produced by different forms of colonialism. Produced in two separate, continuous publications, From Land Grab to LandBack / LandBack Landscape Poems documents a year of texts, projects, and reflections by architects, artists, filmmakers, legal scholars, activists, poets, writers, and educators exploring calls for Land Back from Palestine to Vieques, from South Africa to the Great Plains. The publication includes contributions Sikowis Nobiss and the Great Plains Action Society, Samia Henni, Jason Mena, Pete Goche, Douglas Spencer, Colectiva Feminista en Construcción, Bibi Naniki Reyes Ocasio, Caney Orocovis, CAN Jibaro, Simphiwe Mlambo and students from the GSA Johannesburg, Papel Machete, Marakianí Olivieri, Dima Srouji, Mariana G. Iriarte Mastronardo, Peter Zuroweste, Hasan Shurrab, Nadia Huggins, Samiha Meem, Killian O' Dochartaigh & Edward Lawrenson, Marili Pizarro, Jason Fitzroy Jeffers, Jonathan David Kane, Vashti Harrison, Audrey Jean-Baptiste and Maxime Jean-Baptiste, Alexandra Pagán Vélez, Post-Novis, Hilary Wiese, Rose Florian, Christopher Rey Perez, Luis Othoniel Rosa, and WAI Architecture Think Tank / Nathalie Frankowski & Cruz Garcia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".