Social Capital Initiatives: Employees and Communication Managers Leading the Way?
Bibliographic record
Abstract
Organizations’ development of social capital and their decision to give back to society are becoming increasingly important to the business of managing organizations as much more than profit-driven entities. This article focuses on the rationale for an Australian–Canadian study on employees’ involvement in social capital initiatives and the communication management of these initiatives. As employees are key stakeholders, they play a vital part in achieving organizational goals. This study, a work in progress, highlights an in-depth, qualitative analysis of two organizations—one in Canada and one in Australia—committed to funding community projects as part of their corporate social responsibility development and commitment. The importance of a qualitative study that focuses on subjective components of social capital is that it develops understanding of employees’ attitudes, feelings, and viewpoints. It also begins to investigate why employees might/might not be committed, to organizations’ social capital initiatives. Using an interpretative analysis lens, an understanding of the moral, relational, and communication dynamics is explored. Questions surrounding concepts such as the moral fiber of social capital are highlighted and critiqued in the context of community engagement and what organizations’ social capital investments mean as part of their responsibility to society.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".