Bibliographic record
Abstract
The purpose of this paper is to estimate the intellectual capital coefficient of the firms under study and to study the relationship, if any between intellectual capital and intellectual capital and its constituents. In this empirical paper, analytical research design has been used. Pulic’s VAIC (modified) has been used to estimate the intellectual capital of BSE S&P 500 listed firms from 2007-2016. The data has been collected from CMIE and collected data has been analyzed using Pearson correlation and linear multiple regression analysis using CMIE PROWESS. Findings show that almost all firms under study have a good VAIC score means above 4 and the top VAIC scorer firms were mainly from refinery, metal, cement, steel, tobacco. Correlation analysis and Linear multiple regression analysis show that M/B ratio has a significant relationship with VACA, VAHU, Research and Development (Innovation capital) and Advertisement expenses (customer capital). Year-wise results depicts that value of adjusted R2 is increasing, in 2007 it was just .164 and in the year 2016 it is .607 which infers that VAIC’s role is improving in measuring the market value of firms under study. Year wise analysis shows that adjusted R2 is improving, so findings may serve as significant input for the firms to use intellectual capital as the main factor for improving the market value of firms. This paper will definitely contribute to the existing literature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".