Du pouvoir visuel des nombres comptables: les apports de la phénoménologie d'Husserl
Bibliographic record
Abstract
In this article, we analyse the visual and classificatory power of accounting numbers (Quattrone, 2009; Busco & Quattrone, 2015) by using Husserlian phenomenology to study the restructuring of a firm. We thus explain how accounting images and inscriptions impact individuals at a distance by drawing on the dynamics of perception. By revisiting the Husserlian concept of image-consciousness, we characterize the perception of accounting inscriptions as the product of the interaction between a physical thing (image-thing), a representation (image-object), and a more or less observable reality (image-subject). We reveal the benefits of using this level of analysis by studying how accounting images progressively transformed a painful corporate liquidation into an industrial and financial success. We conducted a longitudinal analysis of the restructuring of a large French firm, which took place in the early 1980s. Through phenomenological analysis, we build on works studying accounting inscriptions and the visual power of accounting, by specifying how they act by drawing on the levers of perception in a flexible, fragile, and sometimes even involuntary way, going as far as to subjugate individuals' representations.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".