Bibliographic record
Abstract
This article deals with the management of instructional developmentprojects for computer-based training (CBT), and is primarily aimed at project managers working with a team of instructional developers for a corporate client. Two issues are discussed: a) estimating the size of a CBT project, and b) performing a cost-benefit analysis. These issues are important for projecting costs, tracking performance and justifying development expenditures.This is a fictionalized case study. The methodology, examples, concepts and estimates are composite sketches drawn from several projects, based upon the author's experiences while working as a CBT project manager. Actual figures and clients have been intentionally obscured to protect the proprietary rights of all parties involved.The reader should be cautioned that the article presents only one approach to project development and estimation. Wholesale application of the approach described is not recommended, as every project will introduce novel interactions of resources and variables which mandate different treatment. Still, it is hoped that the reader will draw upon the ideas presented to refine project management approaches already used.
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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".