Bibliographic record
Abstract
ion and abstract labour Is it only in the process of exchange that labour become abstract? There is a confusion here between the role of abstraction in science and the partial way in which the abstract categories discovered by science become apparent to quotidian perception. Science must always seek the general behind the concrete, the abstract behind the particular. Thus in the development of thermodynamics one has the formation of the abstract concept of heat, which is distinguished from the forms in which it becomes apparent as warmth, temperature or thermal radiation. To measure heat, one needs to co-ordinate several distinct observations and data. If you want to measure the number of calories released by by burning 10 grams of sugar under a bomb calorimeter, one must know the starting temperature of the calorimeter, the volume of water it contains, the final temperature, the specific heat of water, etc. Prior to the development of a coherent theory of heat, and data on the specific heat of water one might come up with regularities like ‘other things being equal, the rise in temperature was proportional to the sugar burnt’, but this is not a measure of abstract heat. The similarity to exchange is clear, a capitalist can observe that, other things being equal his turnover is roughly proportional to the number of
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".