Corporate social responsibility (CSR) of e-commerce business
Bibliographic record
Abstract
The first part of the dataset contains a selection of the top-30 publicly traded e-commerce companies that are leaders in the ranking of the number of employees in 2021 according to Companiesmarketcap (2022). Data on the earnings of e-commerce business structures, as well as on the number of their employees are given. The sample includes developed (Norway, Canada, Germany, Great Britain, Japan, Singapore, South Korea) and developing (Poland, Indonesia, Argentina, China) countries. In the first part of the dataset, a selection of 16 the most responsible companies in the retail industry in the United States in 2020 was formed. Data on the sales of e-commerce business structures are provided. Company sales statistics are taken from Global 2000 for 2021 (Forbes, 2022). Data on the quality of ESG-management (Environmental Score; Social Score; Corporate Governance Score) is also given. ESG management quality statistics are taken for 2020 (according to the results of the year, that is, relevant at the beginning of 2021). Описание датасета на русском языке: В первой части датасета сформирована выборка из top-30 publicly traded e-commerce companies, являющихся лидерами рейтинга численности работников в 2021 г. по версии Companiesmarketcap (2022). Приведены данные о доходах бизнес-структур электронной торговли, а также о числа их работников. В выборку включены развитые (Норвегия, Канада, Германия, Великобритания, Япония, Сингапур, Южная Корея) и развивающиеся (Польша, Индонезия, Аргентина, Китай) страны. В первой части датасета сформирована выборка из 16 the most responsible companies in retail industry в США в 2020 г. Приведены данные о доходах бизнес-структур электронной торговли. Статистика доходов компаний взята из материалов “Global 2000” за 2021 г. (Forbes, 2022). Также приведены данные о качестве ESG-управления. Статистика качества ESG-управления взята за 2020 г. (по итогам года, то есть актуально на начало 2021 г.).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".