<i>Going</i> + COMP projects PP, not AdvP: a reply to Silvennoinen (2025)
Bibliographic record
Abstract
Abstract Silvennoinen (2025) analyzes the stored sequence going forward as an adverb that inherits adverb-class morphosyntax. This reply challenges that categorization on empirical grounds. The construction fails the key distributional test for adverbs: it cannot occur in integrated-medial position between subject and verb (* We going forward will prioritize replication ), the diagnostic slot for core adverbs ( We certainly will prioritize replication ). Analysis of Silvennoinen’s corpus ( n = 1,517) confirms this restriction – apparent ‘medial’ tokens prove either to be NP-internal modifiers or parenthetical supplements, never integrated clausal constituents. Instead, going forward patterns with PP adjuncts, occurring clause-initially, clause-finally, or as supplements. Internally, deverbal going heads the construction and licenses a directional complement forward ( s ), parallel to established deverbal prepositions like according [ to …] and depending [ on …]. The construction thus projects PP, not AdvP, aligning with The Cambridge Grammar of the English Language ’s flexible-complement analysis of prepositions. This case demonstrates that storage and semantic specialization do not force categorical reanalysis.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".