From folk‐tales to shareholder‐tales: semiotics analysis of the annual report
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the use of semiotics analysis to better understand the annual report. It starts with the idea that the annual report is telling stories to the reader. As a form of novel, it can be analysed with the same instrument. Design/methodology/approach The goal here is methodological. It is to propose an organized body of techniques that will allow anybody to conduct analysis from it. Therefore, one example is used uniquely to illustrate the method. The advantages of semiotics over content analysis are numerous. Content analysis remains quite trivial (counting words), while semiotics analysis takes into account the structure of the story at many levels. Findings Framed by the categories of Aristotle's rhetoric, a method is developed that is replicable with a limited background in the source disciplines. The results suggest that the annual report is clearly telling stories and respond quite positively to this kind of approach. Originality/value Although it is often discussed as a general issue, there has so far been no proposal of an integrated method for analysing accounting narratives over content analysis.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".