Finding Places to Make Places
Bibliographic record
Abstract
Placed in the context of ongoing moral disaster, Finding Places to Make Places is a discussion of language and poetic usefulness, specifically how collective discourse survives the unimaginable through personal recourse. It examines the ideology of cultural superiority and intellectual migration in public squares and private homes. With skepticism and love, this poetry and poetics attempts to explain the failure and potential at the heart of revolutions: the impulse to launch the experience of an individual into a communal existence across time. It is meant to speak, with many voices, beyond these known failures and into our many futures. It is a defense of the art of poetry as a means to evoke the necessary accommodations human beings can make to survive what is unsurvivable. The organizing principle for the poetries has been divvied into a curious binary that works to mirror both the similarities and differences in the concepts of each section. These political and/or personal poems demonstrate the culture-bound logistics and flourishings and shortcomings of certain poetic voices during the Cuban Revolution of 1953-59, the Cultural Revolution in China of 1966-79, the Civil War of Lebanon of 1975-90 and the Arab Spring in the MENA region of 2010-11, as well as ongoing revolutions in the lands currently known as the United States and Canada. The poems center around healing these wounded places in the often more ambitiously universal interiors of the psyche.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.030 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".