Telling the privatization story: a study of the president's letter
Bibliographic record
Abstract
Purpose This paper aims to explore the use of narrative instruments, mainly storytelling, to sell the privatization of State‐owned enterprises (SOE) to the general public by their CEO. Design/methodology/approach The paper uses a semiotic analysis approach. It uses specific semiotic analysis instruments: Greimas' actantial model and Propp‐Bremond function model. These main instruments can be backed on time by other devices. The analysis is centered on the president's letters in the pre‐privatization period of Canadian SOEs. Findings The paper finds evidence of the use, by the CEO, of discourse in general and specifically accounting discourse to advocate for the privatization. The paper also finds that the general structure of storytelling in the presidents' letters studied implies the use of narrative instruments to surreptitiously convey specific messages in accordance with the surrounding ideology. Research limitations/implications This paper studies only SOE that had been privatized. However, top managers of every SOE are facing the same legitimating problematic. The context is strictly Canadian. Therefore, further research may examine Canadian non‐privatized SOEs or foreign SOE, privatized or not. Practical implications Privatization is a political decision, i.e. being decided ultimately by citizens. Therefore, CEOs of SOEs do not have to intervene in the debate using their privileged standpoint. Moreover, they will not do it except if backed by politicians promoting the same interests, although tacitly. Citizens must be aware of the manoeuvres done to orientate them toward the “good” decision. Originality/value The paper shows that the apparent objectivity of the financial results can be used to promote political agendas. It says that accounting is not a pure reflection of reality but a language used to promote specific interests. It also shows that accounting is telling stories that are used in other parts of the annual report, such as the president's letter.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".