Bibliographic record
Abstract
In reviewing some of the literature, ideal and non-ideal theories are presented as opposing or at least competing theories, in the same manner as are liberal and progressive theories of education. Some scholars suggest that ideal theory ought to precede non-ideal theory, while others suggest just the opposite. This is referred to in the literature as ‘the priority objection.’ Some suggest we don’t need ideal theory at all and should exclusively use non-ideal theory. Others focus on how this scholar misses the point, that scholar leaves something out, or this scholar has it right and here’s why. My objective in this paper is to argue that aside from important and scholarly discussions, ideal theory and non-ideal theory are artificially polarized. Further, and more radically, characterizing ideal and non-ideal theories as two separate enterprises and as ‘theories’ are category mistakes. Not surprisingly, because of the artificial polarization and category mistakes, the debate is rather confused and stuck. This paper attempts to untangle the confusion and open up the dialogue.
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 | 0.043 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.100 |
| Scholarly communication | 0.015 | 0.049 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".