Analysis of Private Socially Responsible Investment: The Impact of Personal Concern with Corporate Social Responsibility
Bibliographic record
Abstract
Are many years that academics and professionals dealing with the so-called socially responsible investment (SRI). Yet, still it persists today the need of a better knowledge of personal reasons underlying the investment decision. This is evidenced by inconclusive and contradictory findings of decades of empirical research. So, this paper aims at contributing to fill this gap, by deepening whether the level of personal concerns with corporate social responsibility (CSR) and the personal preferences towards the screening criteria adopted by socially responsible funds (SRFs) affect the decision to choose a socially responsible investment. In order to connect the investment choice with the personal concerns for CSR, this study refers to an experimental survey that proposes different investment scenarios and several five point Likert statements referred to corporate social responsibility. Findings confirm that the traditional risk/return trade-off is not sufficient to explain the decision to invest socially responsibly, going beyond a purely financial return. In fact, the level of personal concerns with CSR and the preference for investment screens related to the safeguard of natural environment and human rights incentive individuals to invest in SRFs.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".