Bibliographic record
Abstract
The anaphor agreement effect (AAE) is the crosslinguistic inability for anaphors to covary with φ-agreement (Rizzi 1990, Woolford 1999); languages use various strategies that conspire to circumvent this effect. In this squib, I identify and confirm a prediction arising from two previous observations by Woolford (1999) concerning the scope of the AAE, based on new evidence from Inuktitut (Eastern Canadian Inuit). I propose that anaphors in Inuktitut are lexically specified as projecting additional syntactic structure, spelled out as oblique case morphology; because φ-Agree in Inuktitut may only target ERG and ABS arguments, encountering an anaphor inevitably leads to failed Agree in the sense of Preminger 2011, 2014. I moreover argue that this exact AAE pattern is previously unattested, yet is predicted to arise given the range of existing strategies. Finally, this squib provides evidence against previous detransitivization-based approaches to reflexivity in Inuktitut (e.g., Bok-Bennema 1991).
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".