Bibliographic record
Abstract
This essay is the first in a two-part series. This first installment invites readers to consider a few very basic questions: what does it mean to count words in a text? What happens to the text, and to our understanding of it, when we decompose it into a series of word counts? What relation exists between the textual domain and its numerical image? Or, to restate this question with a nod to literary critic stanley fish, "is there a text in my data?" following one document through a series of typical transformations -- first into a simple list of words and their frequencies, then to a vector of elements in a matrix, and from there through the processes of normalization, dimensionality reduction, and analysis -- this essay argues against the commonly held notion that counting words reduces complexity, suggesting instead that semantic models embed textual objects in highly complex structures that are extremely sensitive to historical context and subtle nuances in meaning. Word frequencies aren't static, given things that simply exist in a text. They're produced through the act of modeling, and the mathematical structures they imply dissolve both words and texts into elaborate systems of mutual interrelation.
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 | 0.011 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.026 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".