Eye-Tracking Experiments in Social and Environmental Accounting Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, we demonstrate the relevance of eye-tracking experiments in social and environmental accounting (SEA) research. Up to now, this type of design has been used in some areas within accounting research, but SEA has been neglected. If one is to adopt a user perspective [Merkl-Davies, D. M., and N. M. Brennan. 2007. “Discretionary Disclosure Strategies in Corporate Narratives: Incremental Information or Impression Management?” Journal of Accounting Literature 27: 116–196; 2011. “A Conceptual Framework of Impression Management: New Insights from Psychology, Sociology and Critical Perspectives.” Accounting and Business Research 41 (5): 415–437], the investigation and the understanding of the way social and environmental information affects user perceptions and decisions requires, among other tools, the use of eye-tracking setups. We discuss the need for eye-tracking experiments in SEA research and provide some preliminary evidence on their usefulness by conducting an illustrative experiment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it