India's mandatory CSR policy: implications and implementation challenges
Bibliographic record
Abstract
The increasing emphasis on social responsibility across the world is not new (Warhurst, 2005) and many countries require companies to disclose information about their environmental, social and employee-related impact, as well as their diversity policy (The Hauser Institute, 2015). India took CSR to the next level by mandating it for all companies through the recently introduced Section 135 in the Companies Act (2013). The provisions of the section require companies to establish a CSR committee consisting of three members of the Board of Directors to develop a CSR policy and review the CSR activities and prepare periodic reports. The above mentioned CSR infrastructure therefore necessitates significant capacity building within companies. With respect to implementation, companies may channel their resources through qualified nongovernmental organisations (NGOs). Consequently NGOs will also require significant capacity building. In this paper we identify the implications of the new guidelines that are worthy of consideration; these implications are for companies that meet the criteria to and therefore must comply with the provisions contained in Section 135, the organisations (including NGOs) that will implement the activities and other general implications. Furthermore the paper suggests mechanisms by which several of these challenges can be met and managed.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".