Application of System Dynamics to Human Resource Management of Canadian Naval Reserves
Bibliographic record
Abstract
The mission of the Canadian Naval Reserves (NAVRES) is to provide trained reservists to meet various challenges of its combat and support elements to enable Canada to meet its objectives in time of peace, crisis or war. In order to sustain effectively and economically NAVRES has to manage an optimal number of the trained reservists in meeting their demands. Over time demand of the trained reservists has been increased. The responsibilities and tasks of meeting the growing demands of the trained reservists can be daunting, risky, and costly without the proper knowledge and tools for evaluating the nature, structure, and potential behavior of the different components of the NAVRES as they relate to the mission of the organization. This paper describes a model that can assist management of the NAVRES to deal with the challenges organization is faced with; as well as plan, manage and drive the future and strategic focus of the organization in its desired direction. Model incorporates the underlying interconnections among different components of the NAVRES that could help to understand the underlying causes of the challenges faced by the NAVRES. The model estimates the requirements of the trained reservists at different levels under various scenarios as well as provides a laboratory environment for the decision makers to test virtually unlimited number of strategies (i.e. “What ifs? ” scenarios) that would accelerate their learning and help them in designing robust and effective strategies to successfully manage their resources strategically.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".