Bibliographic record
Abstract
About 1904 Meinong formulated his most famous idea: there are no empty (non-referential) terms. Russell also did not accept non-referential singular terms, but in “On Denoting” he claimed that all singular terms that are apparently empty could be explained away as apparent singular terms. However, if we take a more careful look at both theories, the picture becomes more complex. It is well known that Russell’s concept of a genuine proper name is very technical; but this is also true of Meinong. Also, according to Meinong we can refer “directly” only to a very special category of ontologically simple objects. However, a very important difference is that, in the domain of Meinongian objects, a plurality of objects always corresponds to each description. Thus, if Meinong were right, there could be no definite descriptions. If we narrow the domain of reference to existent objects, we can secure the uniqueness of the reference object by specifying a collection of predicates that is contingently satisfied by only one (existing) object. But if we operate in the domain of all possible objects, we have to specify all properties that are had by the object in question. It turns out that such a “Leibnizian” specification amounts to the complete description of a possible world.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".