Intellectual Capital: Revisiting an Analytical Model
Bibliographic record
Abstract
The world’s economy is experiencing important changes brought on by diverse factors, namely technological advancements, the appearance and diffusion of personal computers, high-speed telecommunications, and the Internet. These technological changes have influenced the corporate environment, with recent decades denominated as the information economy, the digital economy, the economy of knowledge, a risk society, and the age of quality and innovation. To designate the key concept of the new economic era as “intellectual capital” implies a classification and evaluation effort in order to proceed with its generalization. In today’s world, the study of a model capable of adding explanatory diversity to intellectual capital is very relevant. We observed a true panoply of concepts in the analyzed models based on a literature review. The conceptual evolution during recent decades has motivated many investigations in this field, resulting from the phenomenon of globalization, growing technological innovation, and the observation of significant disparities between the market value and the accounting value of companies. This article describes an investigation carried out, presenting an explicative model of intellectual capital based on four distinct patterns, which are the aggregating factors of the existing conceptual diversity. We present the identification of a model with two axes, x (the type of knowledge, from tacit to explicit) and y (the capital of knowledge, from human to structural), which represents the conceptual diversity mirrored in four quadrants resulting from the research carried out with an initial exploratory study and two following studies with 45 and 72 specialists. In this article we analyze the Martins model, which proves to be essential for systematizing and mapping the dimensions that intellectual capital includes. This model can be used to identify the different aspects of intellectual capital in an organization and thus contribute to its understanding, optimization and good management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".