Capacity Building for Societal Development
Bibliographic record
Abstract
The Problem. Traditionally, the core of human resource development (HRD) has focused on corporate settings and emerged primarily in the United States. The Solution. As the concept has evolved and moved around the world in response to factors supporting globalization, and as academics and practitioners have argued about its definition, HRD has begun to be applied much more broadly, including with geographically dispersed communities and nations. This article presents case studies in which HRD principles and theories have been used for societal development—the general improvement of the welfare of people usually outside of the workplace, primarily in communities. At least one of the coauthors, and usually two or more, have been either involved in or reported on all of the cases included. The Stakeholders. It is critical for HRD academics and practitioners to understand this evolving, broad-based perspective of HRD and participate in its practice, theory development, and research.
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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".