Eliciting Experts' Context Knowledge with Theory-Based Experiential Questionnaires
Bibliographic record
Abstract
This paper describes a useful addition to behavioral researchers' set of research methods, supplementing and extending what can be learned from interviews, surveys, and experiments. The experiential questionnaire (EQ) is designed to study the context within which experts behave, guided by theory about that context and by extensive pre-testing with representatives of the target population of respondents. An EQ is built around expert respondents' experience of their context, the world in which they function, as represented by cases they have experienced and can describe in detail. The value of the data is supplemented by having the respondents choose the cases and do much of the categorization and coding of their own responses. The EQ's questions are worded in matter-of-fact terms familiar to the respondents, to encourage respondents to report their experienced contexts dispassionately. This paper describes the EQ method, with examples from the mostly auditing published studies and suggestions about extensions into other areas of accounting research. The value of studying experts' context is addressed, as are matching of respondents and theory, designing and testing an EQ, and some threats to validity of the data resulting from an EQ. References to related research literatures are included.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".