The Use of Value Engineering in the Evaluation and Selection of Digitization Projects
Bibliographic record
Abstract
Objective - The authors describe a simple and effective tool for selecting digitization projects from competing alternatives, providing decision makers with objective, quantitative data. Methods - The paper adopts the value engineering methodology for the selection, evaluation and ranking of digitization project proposals. Project selection steps are described. Selection criteria are developed. Digitization costs are presented as an equation. Project value is determined by calculating projected performance of digital collections based on the established criteria over cost. Results - Scenarios are presented that evaluate and rank projects based on an evaluation of performance criteria and cost. The communication and use of rating criteria provides selectors with information about how proposed collections are evaluated. The transparency of the process output is easily communicated to stakeholders. Conclusions - Value engineering methodology provides a tool and a process that gives decision makers a set of objective, quantitative data upon which selection of digitization projects is based. This approach simplifies the selection process, and creates transparency so that all stakeholders are able to see why a decision was made.
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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 | 0.075 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".