Eye-Tracking Experiments in Social and Environmental Accounting Research
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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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".