Human capital valuation: tripartite paradigm framework and narratives
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a new measure for organization health. It is proposed that the Donohue tripartite paradigm model can be used to pierce the veneer of the satisficing account to identify the moral appraisal stakeholders have made of corporate strategies based on external (i.e. economic, etc.) standards. Design/methodology/approach A cognitive mapping process through narratives is used to operationalize a tripartite paradigm framework to measure human capital. An existential‐phenomenological approach is adopted to ensure the figural integrity of data. Findings This paper can be viewed as the prototypical development phase for a methodology to support future real‐time ethical inquiry concerning social responsibility within the corporate world. Research limitations/implications The tripartite paradigm model expressed by Donohue was intended for “real‐time” application. This study, however, proposed a retrospective analysis of stakeholder decision‐making within a firm as a means of unearthing any deficiencies that might block the operationalization of Donohue's generalist theory. Practical implications This appraisal can identify the conflict of conscience that characterizes a stakeholder's “lived‐worlds” based on their participation and exposure to company decision making. This diagnostic tool can assist stakeholders in identifying evidence of decline early enough in the history of an organization for proactive remedial action to be taken. Originality/value It is the hope of this study that the proposed cognitive mapping process can derive a measure of organizational health through an existential‐phenomenological approach to ensure the integrity of the data. Ultimately, the aim is that this will be a tool that can explore the phenomenon of misrepresentation and its effect on social cooperation within a market culture.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".