Allocating Limited Resources in a Time of Fiscal Constraints
Bibliographic record
Abstract
Facing a projected $1.4M deficit on a $35M operating budget for fiscal year 2011/2012, members of the Dalhousie University Faculty of Medicine developed and implemented an explicit, transparent, criteria-based priority setting process for resource reallocation. A task group that included representatives from across the Faculty of Medicine used a program budgeting and marginal analysis (PBMA) framework, which provided an alternative to the typical public-sector approaches to addressing a budget deficit of across-the-board spending cuts and political negotiation. Key steps to the PBMA process included training staff members and department heads on priority setting and resource reallocation, establishing process guidelines to meet immediate and longer-term fiscal needs, developing a reporting structure and forming key working groups, creating assessment criteria to guide resource reallocation decisions, assessing disinvestment proposals from all departments, and providing proposal implementation recommendations to the dean. All departments were required to submit proposals for consideration. The task group approved 27 service reduction proposals and 28 efficiency gains proposals, totaling approximately $2.7M in savings across two years. During this process, the task group faced a number of challenges, including a tight timeline for development and implementation (January to April 2011), a culture that historically supported decentralized planning, at times competing interests (e.g., research versus teaching objectives), and reductions in overall health care and postsecondary education government funding. Overall, faculty and staff preferred the PBMA approach to previous practices. Other institutions should use this example to set priorities in times of fiscal constraints.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".