Bibliographic record
Abstract
In most Romance languages, object clitics appear to the left of the verb (proclitics); in European Portuguese (henceforth, EP) they appear to the right (enclitics). Furthermore, several syntactic environments trigger proclisis in EP, which usually have no effect on clitic placement in other Romance languages. These environments can be roughly split into two categories: those in which CP is filled (Wh-questions, focus constructions, subordinate clauses), and those in which a head position between CP and TP is filled (negation, special adverbs). To account for this, I propose that C0in EP has the strong feature [+lexical] which must be checked by a lexical item before Spell-Out. I also propose the following clause structure: TopP>CP>AdvsP>NegP>TP>vP>VP. AdvsP is a functional projection which hosts any one of a small set of special adverbials. If CP is filled by Spell-Out (either in its head or specifier position), the [+lexical] feature will be checked and erased. If not, then C0attracts the closest lexical item.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".