Bibliographic record
Abstract
Internet Financial reporting (IFR) has been growing at a rapid pace since the late 1990's but less is known about the growth in corporate social responsibility reporting (CSR) on the Internet. The Internet is being used at an increasing pace to access companies' financial and non financial information. The aim in this paper is to analyse the disclosure of CSR reporting on companies' websites. More specifically, the sample of 200 companies' websites analysed in this article consist of foreign companies listed on the NYSE, sub classified into regions and industries. The NYSE is the largest stock exchange in the world by dollar value of its listed companies. Literature has suggested that listing on the NYSE has a positive impact on voluntary disclosure which in the context of this article is CSR reporting. CSR reporting has been divided into two dichotomous variables for the purpose of this study: CSR Social and CSR Environmental. A consistent observation in the study is that Environmental reporting has been higher than Social reporting disclosure. The highest percentages of companies with social reporting belong to Switzerland, Guernsey and Canada. The Canadian companies also had the highest proportion of companies with environmental reporting. The national and local influences and type of industry seem to be more dominant factors influencing CSR disclosure rather than the common factor of being listed on the NYSE. This has caused for differences in the nature and scope of the elements disclosed. Due to lack of regulation and being of voluntary nature, the level of CSR disclosure is lower than other elements of Internet Financial Reporting.
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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".