Ontological realism as a validity criterion in second-language strategic competence assessment
Bibliographic record
Abstract
Abstract Strategic competence, conceptualized as the ability to put semantic, grammatical, and pragmatic knowledge into use, is a key element in models of communicative language proficiency but remains a difficult construct to assess in language tests. In the oral proficiency interview (OPI), strategic competence is typically assessed through the use of role-plays with a complication. Assessment of test-taker performance on the role-play is subjective and is contingent on raters accurately identifying interactional evidence of strategic competence. Accordingly, validation of the strategic competence exhibited in role-plays has been mostly interpretive. To obtain evidential support for an interpretive argument that role-plays can indeed isolate and provide assessment evidence of strategic competence, the criterion of ontological realism is applied in this study. Towards that end, eleven samples of English-as-a-foreign-language OPI role-plays with a complication were judged by 52 untrained English native speakers. Evidence in support of the ontological validity of assessing strategic competence via role-plays is presented through analyses of the untrained raters’ judgments, augmented by quantitative analyses that identify sources of variation among the raters, including a post-study additional round of coding in which the notion of “success” in the role-plays was examined more granularly than can be done with dichotomous decisions.
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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 | 0.062 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 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".