Downsizing, Rightsizing, Capsizing—The Outsourcing Saga Roy M. Dalebozik, Eng., Executive Director, Facilities Development
Bibliographic record
Abstract
University presidents are finding vision, positive attitude and creative solution-making increasingly difficult attributes to sustain. The option to outsource often fills this void, providing fast answers and enthusiasm. Downsizing, Rightsizing, Capsizing—The Outsourcing Saga will draw on the experiences of McGill University and other institutions to highlight the concerns and benefits of outsourcing. A model for a structured approach to evaluating outsourcing will be presented, one which supports “change management.” An environmental scan of core competencies related to available internal versus external expertise which forms a framework for selection of outsourcing potential will then be discussed. The need to establish performance measures, both quantitative and qualitative, prior to outsourcing will also be highlighted. And lastly, the criteria for partner/contractor selection and the processes of recent selections in universities will be presented. The opportunities, the future and the results of the outsourcing trend will form the vision of the next decade. Outsourcing Concerns The reality of the situation, as stated by Peter Drucker, is that: In another 10 to 15 years, organizations may be outsourcing all work that is support rather than revenue producing and all activities that do not offer career opportunities into senior management.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".