Pseudo-scoping out of relative clauses: an ‘individual concept’ approach
Bibliographic record
Abstract
Sentences where a definite DP is modified by a relative clause containing a universal quantifier (relative clause DPs), like the supervisor that each volunteer reported to, license readings which carry separate presuppositions of uniqueness and existence for each volunteer—henceforth, ‘varying definite readings’ (VDRs). Barker 2022 argues that these readings involve the universal DP scoping out of the relative clause and above the definite and proposes to analyze them using a non-local scope shifting mechanism, like quantifier raising (QR). In this paper, we argue that this is a case of pseudo-scope. Instead, we argue for a functional interpretation of the DP and propose that VDRs result from the definite DP denoting an ⟨e, e⟩ function, from volunteers to supervisors. As support for this claim, we draw a parallel between relative clause DPs and ‘bare DPs’ (the same definite DP but without the relative clause/embedded universal). We observe that both kinds of DPs license VDRs and thus should be analyzed in the same way—yet bare DPs have no universal to undergo QR. Further support for a pseudo-scope analysis comes from one undergeneration and one overgeneration challenge for the QR approach.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".