Merge over move and the Extended Projection Principle: MOM and the EPP Revisited
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A class of proposals are examined that aim to avoid problems that\n\t\t\t\t arise in various instantiations of the ‘Merge over Move’ (MOM) cost-ofoperation\n\t\t\t\t distinction. It is concluded that while the mechanisms introduced\n\t\t\t\t there exhibit independently interesting features, they subtract substantially\n\t\t\t\t from the interest of the MOM economy of derivation explanations. The\n\t\t\t\t removal of an assumption will then be considered that makes the core cases\n\t\t\t\t involving there-constructions a problem to begin with: that non-finite T must\n\t\t\t\t host a specifier position (checking an EPP/D-feature). Denying the existence\n\t\t\t\t of such features removes the problem that the MOM distinction was\n\t\t\t\t introduced to solve, allowing the core cases of associate-movement vs.\n\t\t\t\t expletive-insertion to arise as a case of true optionality. Consequences for\n\t\t\t\t other phenomena are examined and the proposal is found to be consistent\n\t\t\t\t with much recent research investigating these phenomena.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it