The big ha-ha, a chaotic exploration into finding lost water
Bibliographic record
Abstract
Attempting to bound my project into some concrete truth has been like trying to grasp a fist full of Manitoba clay; the tighter I squeezed, the more of it slipped through my fingers. I would then scoop up the droppings, compact it again, and squeeze with all my might; only to find little left in my hand once more. The repetition of this movement became a mantra, and as the project began to sway back and forth, I began to meditate. I closed my eyes and instead of looking at the clay my hand was trying to force into shape, I saw for the first time the mounds that had fallen at my feet. When I opened my eyes again I saw a river that flowed red. I noticed the mounds, like the clay ideas that had fallen from my hands, erupting from the surface of the water. There was a sharp bend in the river, and in the crook of that bend was a grove of maple trees sheltering my dad's maroon 1962 Ford where my mom and dad were dating. I looked up and saw that the moon was full and the stars were right, and I knew that a practicum was about to be born. (Abstract shortened by UMI.)
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 | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.022 |
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".