Multinational Corporation Codes of Conduct: Governance Tools for Corporate Social Responsibility?
Bibliographic record
Abstract
ABSTRACT Manuscript Type: Empirical Research Issue: We investigate the assumption found in code and corporate social responsibility (CSR) literature that suggests codes are primarily associated with the CSR practices of an organization. Research Findings/Results: A web‐based study of 150 corporations from three different countries indicates there is little empirical support for this link between codes and CSR. Thus, if a corporation has a code, it is more likely used to govern traditional business concerns, such as compliance with third party governance requirements, internal issues such as conflict of interest, bribery and corruption, insider trading, etc. This is consistent across all three countries. Therefore we must be cautious against assuming a link between codes and CSR. Evidence of the different governance contexts is also briefly discussed. Theoretical Implications: Findings are addressed to theoretical debates about the construction of corporate identity, the amoralization of business, and the globalization of management practices. Practical Implications: Stakeholders must be careful in assuming that the presence of a code indicates CSR commitments or behavior. Stakeholders need to look at the content of the code to confirm or deny this assumption, particularly such stakeholders as investors who tend to use the existence of a code as evidence of CSR practices to tick “check the box.”
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 | 0.015 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".