Doing well by doing good? Normative tensions underlying Twitter’s corporate social responsibility ethos
Bibliographic record
Abstract
This article examines the rhetoric of Twitter.com in order to gain insight into the company’s normative self-understanding, or ethos. From a business ethics perspective, we analyze Twitter’s ethos in relation to debates around democratic communication and corporate social responsibility (CSR). Partly thanks to its CSR strategy, Twitter has acquired the critical mass of users necessary to successfully establish a robust and financially viable social network. Despite its success, however, we argue that Twitter does not sufficiently address three ethical implications of its strategy: (1) from an ethical perspective, Twitter mainly seems to employ an ‘instrumental CSR’ ethos that fails to properly recognize the moral rights, responsibilities, and strategic challenges of corporate actors with regard to their stakeholders; (2) this issue becomes all the more pressing because online social networks to a certain extent have taken on the role of quasi-governmental bodies today, regulating what their users can and cannot do, thus raising questions of accountability and legitimacy; and (3) in Twitter’s case, this leads to normative tension between the site's rhetoric, which is centered around civic motives, and the way its Terms of Service and licensing policies seem to favor its commercial stakeholders over its noncommercial ones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".