A Framework for Capacity and Operations Planning in Services Organizations Employing Workers with Intellectual Disabilities
Bibliographic record
Abstract
This paper presents an integrated framework for capacity and operations planning in Spanish sheltered employment centers (SECs). Employment provides socio-economic opportunities for people with disability. Well functioning SECs that provide opportunities for people with and without disability to work alongside each other are an important component of Spain’s current labor market. To be economically sustainable, SECs need to satisfy their clients expectations in terms of price, flexibility and performance, whilst taking into account and strengthening the diverse skills and abilities of their workforce like specific learning and forgetting processes. The SEC studied herein, a non-for-profit organization whose mission is to create employment for people with disabilities its workforce is deployed directly on clients’ premises. Efficient management across this multi-site environment, whilst supporting the diverse employment needs of its staff, is of paramount importance. This paper contributes an integrated framework to support SEC capacity and operations planning, which prioritizes the explicit training needs of workers with disability at all levels (strategic, tactical, and operational) as a lever for achieving the organization’s goals.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".