Bibliographic record
Abstract
I have a friend who critiques my urban lifestyle. He lives in a golfcourse suburb, or, as he puts it, “nature.” While his position is easy to mock, more interesting is the puzzle his subjectivity and mine present: what kind of representational apparatus would account for his vision of nature and my mockery of his vision? Do we balance each other on a semiotic teeter-totter? Intrigued, I carried out a “phenomenological” experiment one Sunday afternoon by driving through a Calgary golf exurb named Elbow Valley, a neighborhood in a gorgeous section of foothills, well treed, designed with views and seamed with parklets and pathways. For the first ten minutes I marveled, and thought about complexity and integration; after ten I found everything tedious. I couldn’t discount the cliched sense that I was touring a film set or section of Disneyland. Something felt missing-not absent, but attenuated, like an imbalance between order and turbulence. Surely this landscape, like all others, was haunted by excess. But the repressions felt so strong that I doubted there was any chance of genuine novelty or creative advance ever arising. Of course, my experiment was really anything but phenomenological, since I could not remotely bracket the local; indeed, the local-from the architecture to my preconceptions—suffused my consciousness. How much did it cost to live in this nature, so carefully bundled around and into this golf course? How did the landscape loan its meanings to the course, and the course, in turn, to the exurb, in a cycle both centrifugal and centripetal?
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".