Restoring social legitimacy: discursive strategies used by a pharmaceutical industry leader
Bibliographic record
Abstract
Purpose The aim of this paper is to understand discursive strategies used by organizations to restore their fading legitimacy. This longitudinal case study is built around two events representing a threat to the legitimacy of the pharmaceutical industry. This study describes some subtle techniques employed to restore legitimacy during those difficult periods. Design/methodology/approach This research analyzes the president's letter of the annual report using semiotic tools designed to catch the essence and goals of the narrative sections. This case study covers 20 of Pfizer most recent annual reports (1988‐2007). Findings The paper suggests that some narrative sections are built to protect legitimacy on two fronts. Most of the time, the discourse maintains legitimacy in front of the salient stakeholder by presenting the firm's main “object of desire” as the enhancement of shareholder's value. In a period of crisis, the narratives are built to restore legitimacy in the eyes of the general public. To do so, they substitute a screen object (related to the theme of the crisis) as the goal of the companies' action. Research limitations/implications The annual report appears as a selling document, discussing “political issues” rather than economic rendition of accounts. However, it is impossible to expose the controversies here, and it is not the purpose of the paper. Originality/value The paper brings together multiple elements of narrative sections to show how pharmaceutical firms built their discourse to restore legitimacy by adapting their defensive texts to specific screen objects as a response to a crisis.
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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".