CAPITAL BUDGETING DECISION – A FUZZY GOAL PROGRAMMING APPROACH
Bibliographic record
Abstract
This paper has provided a critical review of the Capital Budgeting and an attempt to re counsel it with the reality that faces the financial executive efficiencies of model for capital budgeting which can be justified only in relation to the earning for objectives and goals.If the objective is given the top priority by management it minimizes the next year's earnings per share, it may be fool hardy indeed to drop a capital budgeting technique that attempts to minimize the net present value of stream of future cash flows.Because of the inherent differences between accounting income and incremental cash flow would be only by coincidence that an optimal decision would result.The responsibility vests heavily on the shoulders of top management to refine clearly and specifically, what the objectives of the capital budgeting system should be without definition of measure of its effectiveness and one model appears just as acceptable as the other one.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.027 | 0.010 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".