Bibliographic record
Abstract
Why does place matter?"Location is everything" is the mantra of the real estate agent.But what about "ideal estate," the immaterial and invisible properties that lurk in a location?Ralph Waldo Emerson, surveying the farms of New England, insisted that "there is a property in the horizon which no man has but he whose eye can integrate all the parts, that is, the poet.This is the best part of all these men's farms, yet to this their land-deeds give them no title."We must now add to the poet the scientist who exposes the potentials and the poisons hidden beneath the surface, the photographer or painter who reframes or overwrites the landscape with a critical topography, the historian who reminds us what took place in a place while erasing its traces, or the journalist or detective who reveals what is taking place in the present moment.Place is the foundational term of a critical topography that attempts to triangulate the properties of a site and a situation.Both a noun and a verb, a material thing and an intentional act, place secures the grounding of landscapes (views, pictures, vistas) and spaces (practices, processes, movements)."I placed a jar in Tennessee" is Wallace Stevens's declaration of poetic sovereignty over a state, a gesture as arbitrary as the juridical boundaries of a "state" or (in Canada) a "province."
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.303 | 0.158 |
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".