Positive and negative effects of CSR : the case of disabled workers integration in the portuguese context
Bibliographic record
Abstract
Corporate Social Responsibility ability to respond to environmental and social problems is beneficial in different ways. The integration of People with Disability (PwD) in the workforce is one expression of CSR that can have important positive impacts. However, little is known about the specific impacts of the colleagues responsible for the integration and supervision of PwD. This is the major objective of this research. Two studies, based on a survey and individual interviews, provide some initial evidence on this matter. We found that in Portugal the inclusion of PwD is considered positive. Thought, it is a process held informally my companies, where stigmas and mistrust concerning the ability of these individuals to perform the job, can still be found. The results also show that experience, sensibilization, and preparation are the key to promote better and impactful integration. The insufficient awareness regarding disability suggests a collective and global effort, led by governments and organizations, that should alert and incentivize a whole nation to break this social barrier and turn it into opportunities. Well-structured and implemented inclusive programs can easily create win-win situations. By promoting all their employees’ engagement and potential, organizations will enhance individual and collective results, levering its differentiation, and competitiveness. We argue that the inclusion of PwD is an HR initiative valuable for any organization and indispensable for social progress.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".