Bibliographic record
Abstract
Anaphora describes a dependence of the interpretation of one natural language expression on the interpretation of another natural language expression. For example, the pronoun ‘her’ in (1) below is anaphorically dependent for its interpretation on the interpretation of the noun phrase ‘Sally’ because ‘her’ refers to the same person ‘Sally’ refers to. - (1) Sally likes her car. As (2) below illustrates, anaphoric dependencies also occur across sentences, making anaphora a ‘discourse phenomenon’: - (2) A farmer owned a donkey. He beat it. The analysis of anaphoric dependence has been the focus of a great deal of study in linguistics and philosophy. Anaphoric dependencies are difficult to accommodate within the traditional conception of compositional semantics of Tarski and Montague precisely because the meaning of anaphoric elements is dependent on other elements of the discourse. Many expressions can be used anaphorically. For instance, anaphoric dependencies hold between the expression ‘one’ and the indefinite noun phrase ‘a labrador’ in (3) below; between the verb phrase ‘loves his mother’ and a ‘null’ anaphor (or verbal auxiliary) in (4); between the prepositional phrase ‘to Paris’ and the lexical item ‘there’ in (5); and between a segment of text and the pronoun ‘it’ in (6). - (3) Susan has a labrador. I want one too. - (4) John loves his mother. Fred does too. - (5) I didn’t go to Paris last year. I don’t go there very often. - (6) One plaintiff was passed over for promotion. Another didn’t get a pay increase for five years. A third received a lower wage than men doing the same work. But the jury didn’t believe any of it. Some philosophers and linguists have also argued that verb tenses generate anaphoric dependencies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.161 | 0.082 |
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".