A proposal for the development and operationalization of a PPBS approach to fund allocation in the social service field
Bibliographic record
Abstract
In recent years considerable interest and concern has been expressed with regard to the nature and quality of decision-making at federal, provincial and local levels in both public and private sectors. This concern, stimulated by the unintended consequences of hastily and poorly developed decisions and by the accelerating rate of social change, has prompted the allocation of substantial resources in many organizations and departments to develop more rational approaches, designs and models for decision-making. In the social welfare field at federal, provincial and local levels in both public and private sectors there is also an increasing demand for better models and approaches for decision-making, accountability and performance analysis and general administration. This thesis will suggest the development and operationalization of a Planning, Programming Budgeting System (PPBS) as a means of meeting some of the above demands and will focus upon the development of a comprehensive decision-making model which will facilitate: 1. the rational and on-going identification of community need; 2. the establishment of periodic community priorities; 3. the formulation of specific planning/budgeting objectives; 4. the allocation of resources to alternative service or program strategies to achieve defined objectives; 5. the on-going evaluation of^results for the next planning-programming-budgeting cycle. While the setting in which this thesis was developed in the United Way of Greater Vancouver, the model developed is applicable to the general
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".