Engaging Employees Through Corporate Social Responsibility Programs: Aligning Corporate Social Responsibility and Employee Engagement
Bibliographic record
Abstract
The message is clear: people want to work for organizations where they feel they are engaged and learning. Recently, the organization Benevity in Canada stated that "Today’s employees are expecting a greater sense of purpose in the workplace. In fact, 83% of Millennials say they would be more loyal to their employer when they feel they can make a difference on social and environmental issues at work." Companies that engage in social and environmental stewardship also benefit from employees who are more aware and involved. Once the Corporate Social Responsibility (CSR) strategy and programs have been defined, how do you ensure your communications strategy, plan and actions are aligned and will help deliver the desired results to get employees interested and engaged? An online survey with 100 respondents in Canada demonstrated a clear shift from years ago where now current applicants research a company’s CSR information when applying to work there. And once working inside the organization, employees notice CSR initiatives and want to get involved.
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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".