Social and Environmental Disclosure by Parastatals and Companies Listed on the Botswana Stock Exchange
Bibliographic record
Abstract
The need to communicate social and environmental effects of business operations to society has been with us forover four decades now. However, due to diverse factors the pace of introducing social and environmentalreporting in developing countries has been slow. The purposes of this study are: to determine whether companieslisted on the Botswana Stock Exchange and Parastatals in Botswana report on social and environmental issues intheir annual reports and to what extent they disclose such information; to establish whether there is a differencein the level of reporting between listed companies and parastatals; and to determine whether size, type ofindustry and ownership influence the extent of disclosure. All listed companies and parastatals operating inBotswana formed the population of this study. Secondary data obtained from annual reports was reviewed andcontent analysis was employed to determine the extent of social and environment reporting. The extent ofdisclosure was measured using number of sentences describing the key themes such as human resources,environmental issues and community involvement. Regression analysis was used to find the association betweendependent variable (extent of disclosure) and three independent variables namely size of the company,ownership and type of industry. The results suggest that social and environment reporting exists in Botswanaentities and listed companies tend to disclose more than parastatals. The findings also show that size, type ofindustry and ownership are not good predictors of the level of social and environmental disclosure in Botswanaentities.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".