Gender and person agreement in Cicipu
Bibliographic record
Abstract
I, Stuart John McGill, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. The Cicipu language (Kainji, Benue-Congo) of northwest Nigeria has the kind of robust noun class system characteristic of Benue-Congo languages – GENDER agreement is found on a great many agreement targets inside and outside the noun phrase. For a number of these targets, gender agreement is in competition with a separate paradigm, that of PERSON agreement. The dissertation focuses on the distribution of this alternation with respect to subject prefixes, object enclitics, and pronouns, based on a corpus of 12,000 clauses of spoken language. The alternation proves to be complex to describe, involving a constellation of lexical, phonological, morphosyntactic, semantic and discourse-pragmatic factors. In particular, both animacy and topicality are CONDITIONS (Corbett 2006) on agreement. While inanimate or animal participants normally trigger gender agreement, if they are
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".