Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze the ethics of a specific communication strategy to support the contention that ethics needs to be an integrated operational consideration in the corporate communication planning process rather than an afterthought. Design/methodology/approach Using the marketing communication strategy referred to as disease branding as a case‐in‐point, the “Five Pillars of Ethics for Public Communication” provide a framework for analysis of the need for making ethics an operational consideration in planning. Findings Communication strategies attempted by organizations today are subject to public criticism. Disease branding, a prime example, is paradoxically a “non‐branded” approach to marketing pharmaceuticals directly to consumers. Pejoratively referred to as disease‐mongering, this promotion of diseases rather than drugs neatly side‐steps the increasing criticism and even legal obstacles that face or threaten to face direct‐to‐consumer advertising of branded, prescription drugs. It is an innovative, non‐traditional tactic that has been enormously successful in widening markets for specific drug preparations. Application of the “Five Pillars” for ethical analysis finds that this strategy fails to meet the acceptable ethical standard in four out of five. Research limitations/implications This study is limited to the application of one approach to ethical evaluation, although it is one that encompasses a number of widely accepted standards for practice. Practical implications An ethical analysis using the “Five Pillars” can be implemented by any corporate communication professional as a litmus test for determining the ethics of strategies under development during the operational planning process. Originality/value This paper fills a gap in the information available to corporate communication professionals about how to operationalize ethics.
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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".