The mobilization of accounting in preening for privatization
Bibliographic record
Abstract
Purpose The paper sets out to examine the use of accounting as part of the privatization process of a national railway in Canada. The argument is that proponents of the privatization used accounting strategically to justify and sustain the privatization. Major societal events, such as the privatization of national assets, merit close scrutiny so that an accounting world thus constructed should not be permitted to pass unchallenged. Design/methodology/approach The paper explores the way in which accounting language, concepts and information were deployed in the prospectus issued in support of the initial public offering of shares by the Canadian government. Findings Evidence is found suggesting that the vagaries of accounting language were marshalled to sustain a self‐fulfilling prophecy of success. Research limitations/implications Case studies possess both the strength of specific instance detail and interpretation, and the ostensible weakness of interpretation of a sample of one. But such research may provide for reframing conceptual perspectives and contribute to stimulating additional efforts at interrogating accounting language's roles in major social change events. Practical implications The paper strongly endorses a critical analytical perspective by all those affected by major social change, such as privatization, in which accounting language often plays a persuasive but subtle role. Originality/value Individuals, groups, employees, managers, customers, and others, including the public‐at‐large, who are potentially impacted by privatizations, are reminded that accounting is not an innocent bystander in the political maneuverings associated with a privatization. Accounting does not axiomatically provide an objective measure of some underlying financial truth, but is part of an arsenal of rhetoric to achieve political ends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.046 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".