Bibliographic record
Abstract
Recent audit reports by the Auditor General noted the potential impact to Canadian Armed Forces readiness due to sub optimal Defence Supply Chain performance. To identify the root cause of performance deficiencies and assess the adequacy of existing training, a systematic approach was employed to identify the knowledge and skills required by each Supply Chain role to perform their share of tasks across 39 Processes. Based on the Department of National Defence Supply Administration Manual (SAM), the project team mapped Processes and Tasks to all applicable Supply Chain Phases. Processes and Tasks were also mapped to each role and the training priority was determined based on Difficulty, Importance and Frequency (DIF) analysis. We then mapped topics/teaching points from relevant course to existing processes and Tasks; and generated a list of processes and Tasks with “adequate”, “limited” or “no” curriculum to support the acquisition of requisite knowledge and skills for each role. The analysis revealed: • All roles contribute heavily to the overall success of the Supply Chain in an integrated work environment – necessitating an understanding of the impact of their work on others. • Developing curriculum incrementally over the years based on specific, sometimes narrow needs/performance and without a comprehensive map as outlined above yielded inefficient learning solutions. • Developing role-based solutions in parallel with process-based curriculum resulted in gaps and duplication of effort. This paper reaffirms the need for “getting back to basics”. A thorough analysis and mapping of actual work/role requirements based on an authoritative reference, using a systematic process enabled by a leading-edge Training Management System, will provide a robust analysis framework. Training gaps and overlaps will become evident, and a blueprint for a comprehensive re-organization of the curriculum will naturally emerge.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".