Embedding corporate responsibility through effective organizational structures
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the ways in which companies are embedding the corporate responsibility function in different organizational structures, and to identify, when possible, best practices related to organizational structures which have proved effective in managing corporate responsibility that can be applied by any organization, regardless of size or industry sector. Design/methodology/approach The authors developed and applied a methodology, in the form of a questionnaire, covering more than 40 aspects to describe what companies are doing to integrate the corporate responsibility function in their organizational structures. The design of the survey was based on available literature as well as their own professional experience answering questions commonly received from clients in Latin America. The questionnaire was then applied to a small sample using companies' public information from reports and company web sites. Findings The application of the questionnaire on a sample of Chilean companies using their public information tested the tool as valid and fit for the designed purpose. The main conclusions were that CSR structuring and CSR strategies are both strongly associated with the size of the company in terms of number of employees and revenues. Originality/value Many questions arise when the task of implementing CSR is proposed and Latin American companies are trying to apply best practices by learning from the experience of companies with longer histories in CSR matters. However, trends are not uniform and different organizations are taking a variety of pathways in the process of CSR implementation. This paper offers a general vision of how companies are making the effort to implement CSR best practices, in terms of structure, strategy and scorecard; and presents a simple tool to assess the gaps, if any, in the effective embedding of corporate responsibility on organizational structures.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".