Value‐added reporting as a tool for sustainability: a Latin American experience
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a collection of ongoing experiences with a value‐added reporting model in Latin America, positing its pertinence with regards to CSR accountability. Design/methodology/approach The paper utilises a qualitative methodology in which a series of semi‐structured telephone interviews and/or e‐mail questionnaires with managers from six reporting companies in Latin America (Chile, Colombia, Uruguay) was conducted. The fact that one of the authors of this paper created the reporting model facilitated easier access to company managers and a deeper understanding of each situation. A literature review from European, US and Latin American sources provides a framework for discussion. Findings The paper illustrates how value‐added statements (which are based on conventional financial accounting) can provide relevant information for CSR accountability. The variety of experiences shown (different industries and diverse company ownership in separate countries) may suggest the wide potential of this reporting model. Research limitations/implications As the paper deals with a recent, ongoing experience (this model has been in use for the last six years only), the results have to be treated with caution. Even though many firms are interested in adopting this value‐added model, there are currently fewer than 20 reporting firms using it. Social implications The paper aims to position value distribution and its accountability as relevant issues in CSR, particularly for developing countries. In addition, such an intuitive model might more easily reach the general public, something that rarely happens with conventional CSR reporting models. Originality/value This is the first academic paper that demonstrates the application of this reporting model (though the authors already published a practitioner‐oriented article in Spanish). Furthermore, there are few documented cases of value‐added reporting experiences in emerging markets, particularly in Latin America.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".