A Contextual Approach to the Study of Discourse Anaphoric Expressions: On the Probabilistic Nature of Theoretical Predictions
Bibliographic record
Abstract
Anaphoric expressions are among the most frequent language forms which depend on context for their resolution. Among efforts made in theorizing referential choice, distance approaches take into account how accessibility/continuity is reflected by choice of referring expressions. The thing is that individual’s choices are made under major and minor influences which affect the variable predictability of anaphoric expressions. In order to investigate the variability of individual writings in a free writing task (the present paper aims to investigate the variability of individual writings in terms of the use of anaphoric expressions in a free writing task). Narrative data was collected from 10native writers (of English/Persian in University of Isfahan) by presenting them a seven-minute soundless action movie and asking them to narrate its story. The results show a fair degree of diversity in varying frequencies of pronouns and Full NPs. It is suggested that diversity might be caused by two factors: referential strategies of text producers who consider the helping role of context as well as the degree of referential distance, referential ambiguity and salience.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".