Robust inference processes in expository text comprehension
Bibliographic record
Abstract
Expository text offers particular challenges to the reader because of the abstract and unfamiliar concepts that it presents and its distinctive structure. The present study had three interrelated aims: (1) It examined the impact of appropriate connectives on the reader's derivation of causal bridging inferences from expository text. (2) It scrutinised texts longer than the ones that we had previously examined (Singer, Harkness, & Stewart, 1997), which in turn made it possible to (3) evaluate the impact of position in the text on inference processing. In Experiments 1a and 1b, a joint profile of target reading times and inference answer times indicated that the inspected inferences reliably accompanied reading only in the presence of appropriate causal connectives. The connective-present conditions of Experiment 1 replicated our previous findings using shorter texts (Singer et al., 1997). Experiment 2 indicated that these inference processes are unaffected by text position. We interpreted these findings with reference to the inference validation model (Singer, Halldorson, Lear, & Andrusiak, 1992).
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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 | 0.005 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".