Bibliographic record
Abstract
How To: Mind The Moon was a workshop that culminated in the generation of a perverted material library by Lev Bratishenko, Francelle Cane, Anastasia Kubrak, Jane Mah Hutton, Marija Marić, Amelyn Ng, Bethany Rigby, and Fred Scharmen. "Space mining, the extraction of resources in outer space, is a project tending to become a reality. In search of rare minerals, metals, and other valuable materials, the wild imaginaries of extraction-driven growth have literally transcended the boundaries of Earth. This displacement of resource exploitation from the exhausted Earth to its ‘invisible’ hinterland—the Moon and other celestial bodies—calls for an urgent debate on the impact this shift will have on our understanding of land, resources, and commons. How to: mind the moon offers another way of reading five lunar materials: regolith, lunar dust, solar wind, seconal sodium, and aluminium. A perversion of the format of a material sample and datasheet—technical documents commonly used in material science to describe chemical and mechanical properties of materials—the workshop outlines another kind of material library, that which goes beyond the perceived scientific neutrality of materials. Instead, it frames the political, social, environmental, and cultural conditions of materials, both as a physical matter and a form of fiction." [Workshop Description from CCA: How To Mind The Moon] How To: Mind The Moon was a workshop hosted by the Canadian Centre for Architecture. This project would have been impossible without research conversations with Abigail Calzada Diaz, Ian Crawford, Alice Gorman, and Rory Rowan. How to: mind the moon was developed in collaboration with the Luxembourg Pavilion at the 18th International Architecture Exhibition – La Biennale di Venezia. The outcome of the workshop, a material library, was produced and exhibited as part of the exhibition Down to Earth of the 2023 Luxembourg Pavilion in Sale d’Armi, Arsenale di Venezia, curated by Francelle Cane and Marija Marić.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.083 | 0.041 |
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".