How Organizations Build Long Term Social Capital Value: The Mechanism of Social Capital Appreciation
Bibliographic record
Abstract
The term social capital is an often used in organizations, but it is rarely described in detail or quantified as a component of an organization’s value creation potential. In this conceptual paper we address this practical and research gap by proposing initiatives along two complementary dimensions: First, we propose an initial step on the road to clarifying and quantifying the behavioral drivers that constitute social capital and describe how the value of social capital increases, or appreciates, over time. We do this by identifying twelve specific behaviors from the literature, collectively called the APPRECIATOR Algorithm, that individual team members can enact individually and also reinforce in others as they contribute individually and in collaboration. Second, we introduce a design for an AI-enabled work-team engineering platform that supports the gathering, analysis, and use of previously hidden human interaction data to quantify and increase the value creating potential of human and social capital. The platform supports individual professional development by delivering context-specific, personalized smart-coaching insights and opportunities for virtual human mentoring on the user’s smartphone. Importantly, each user owns and controls its data. Privacy is secure and protected because each user’s data can only be accessed and used by that particular user.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".