Bibliographic record
Abstract
“Queer demarcates not a positivity but a positionality, vis-à-vis the normative, a positionality that… \nis in fact available to anyone or thing that is marginalized.” - (Halperin 62) \nDespite the growing relevance of recreational dispensaries, currently there is a severe \nlack of architectural inquiry that critically looks at the socio-spatial development of the cannabis \nstorefront. Furthermore, heteronormative discussion of dispensaries willfully ignores or overlooks the \nqueer roots of legal cannabis architecture in the HIV-positive LGBT community of San Francisco. \nThe research phase of this project parallels queer space theory with Everyday Urbanism and offers \na new lens to understand the early cannabis dispensary as a queered space. \nWhat is queer space, or rather, what does it mean to queer? Reed bemoans the idea that \nbecause queerness is “... an ineffable ideal of oppositional culture, [and] is so fluid and contingent \nthat the idea of a concrete queer space is an oxymoron” (Reed 64). Instead, he along with \nother queer space theorists, such as Aaron Betsky, layout a series of qualities that can be used \nto better understand queer space as a process based attitude towards design, something that is \nactionable, a queering of architecture. \nQueering Cannabis is an installation-based project informed by the queer roots of legal \ncannabis architecture. Functionally, the project posits that the queering of architecture offers \nan actionable design attitude, which works in opposition to the erasure that has taken place \nthrough the typification of the cannabis dispensary over time and current policies that stigmatize \nthe consumption of cannabis. Sitting somewhere between performance, tagging, and protest, \nQueering Cannabis initiates a sequence of enactment, loss, and construction informed by the \nsymbolic queerness of smoke, ash, and residue. \nSeattle, Washington, is the primary site of investigation and deployment of this theory. \nSeattle was chosen for the relatively long period legal cannabis architecture has had to develop, \ngiven Washington state legalizing recreational cannabis in 2012 (Reiman 351).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 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".