Bibliographic record
Abstract
I n chapters 1 through 4, i have presented a fair amount of empir ical evidence for various positions, observing that X and X (2003) “found” that Y was the case, or that the results of X (2001) “suggest” that Z is true. There are several ways you can take statements like this. You may follow me in taking such evidence as constituting a fairly convincing case in support of position Y or Z. Alternately, you may be committed to empirical inquiry, but see these particular cases as flawed studies that – because of poor experimental design or variables not considered by the experimenters – fail to demonstrate what they are intended to demonstrate. Finally, you may be a social constructivist and feel that any reference to empirical evidence is merely a bit of rhetorical handwaving: supposedly “empirical” studies reveal nothing outside of the presuppositions and expectations of the experimenters involved, and thus they are not in any way epistemologically superior to other forms of rhetorical assertion. In our modern Western culture, however, “science” has been accorded a privileged status as the new official religion, so dressing up assertions in a sciencelike garb is a very effective strategy. To a committed postmodernist, my push for vertical integration is best seen as a rhetorical gambit designed to advance my personal opinions by borrowing some of the prestige of science, and by throwing in a bunch of impressively scientific-sounding references to give my arguments a specious appearance of gravitas.
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 | 0.028 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".