Corporate Social Responsibility: the Covid-19 Test. The Response Through a Case Study Comparison in Italian Fashion Companies
Bibliographic record
Abstract
This study analyzes how, during the Covid-19 pandemic, macro, meso and micro-level triggers have promoted the development of Corporate Social Responsibility (CSR) in companies throughout the world. We propose a theoretical framework to highlight how these triggers have influenced the institutionalization of CSR in companies, implementing actions to support the Italian healthcare system, ensure the health and the safety of the population, and mitigate any social and economic problems that could be generated by the pandemic. The fashion industry is among the economic sectors that have been particularly affected by the crisis and has suffered greatly from the effects of the decision made by the Government to limit the diffusion of the pandemic. A case study comparison in Italian fashion companies is proposed here to highlight how all of these CSR actions can be interpreted, considering a more general principle of CSR promoted by owners and managers who, voluntarily, have taken decisions for the benefit of the community and their employees. These actions have both theoretical - considering future research lines on CSR - and practical implications on how companies should consider their stakeholders, in particular, employees and society as a whole, going beyond the human resource strategies and the classic commitment through philanthropic activities.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".