Uniqueness and negative stance:<i>only if</i>and<i>if only</i>
Bibliographic record
Abstract
If only there was a camera that captured smells. (AG. TB.197) Only , like the exceptive semantics of unless , conventionally and explicitly modifies the inferential structure of conditional space construction. These modifications in inferential structure affect the informational structure in ways which interact with the clause-ordering principles discussed in the previous chapter. The clause ordering further interacts with scopal relations of if and only , and with the polysemous semantics of only – making these constructions an ideal laboratory for the interaction of multiple parameters involved in grammatical space building. Only if and if only take part in distinct constructions, each of which inherits structure from the basic if -conditional construction, but each of which also inherits or takes motivation from other orthogonal constructions. In addition, if only (in contrast with the more compositional only if ), takes part in special constructions whose form and meaning are not compositionally predictable. Mental Spaces Theory, however, gives us convenient formal ways to express some of the contrasts between these two classes of constructions, and between each of them and central if -conditionals. We shall first examine only if . Only if : space uniqueness and negative meaning In understanding the combinatorial semantics of only and if , the first relevant factor is scope interaction. Only combines compositionally with sentence semantics in different ways depending on its scope relation to the clause. In the standard case of only if, only precedes and has scope over the entire adverbial if -clause constituent of the main clause.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".