Using decision analysis to explore cable television delivery
Bibliographic record
Abstract
Purpose The purpose of this paper is to demonstrate the efficacy of decision analysis in determining the most efficient strategy for installing cable television in the residence halls of Bucknell University. Design/methodology/approach The decision analysis model compared five distinct approaches for achieving and maintaining a successful delivery of cable television service to students enrolled in this private, residential institution. For each alternative, the model incorporated installation costs, likelihood of installation failure, installation failure costs, likelihood of obsolescence and obsolescence-related costs. In addition to considering the trade-offs between cost, timing and riskiness of the various alternatives, a thorough set of sensitivity analyses was performed to gain insight into the parameters that most strongly influence this decision-making process. Findings The quantitative model advocated the adoption of the university’s data network as the mode for cable delivery. Sensitivity analysis further supported this notion. Practical implications The analysis of this problem incorporated the knowledge and judgments of senior administrators and staff members, thus demonstrating the critical contributions offered by subject-matter experts in advising, informing and launching successful decision analysis projects. Incorporating stakeholder viewpoints enhances model understanding and, eventually, model implementation. Decision analysis represents a powerful approach in communicating uncertainties and advising on the benefits of particular alternatives. Originality/value To the best of the researchers’ knowledge, this paper represents an initial attempt to investigate cable delivery options within a decision analysis framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".