What Is Public Agency Strategic Analysis (PASA) and How Does It Differ from Public Policy Analysis and Firm Strategy Analysis?
Bibliographic record
Abstract
Public agency strategic analysis (PASA) is different from public policy analysis because public agency executives face numerous constraints that those performing “unconstrained” policy analysis do not. It is also different from private sector strategic analysis. But because of similar constraints and realities, some generic and private sector strategic analysis techniques can be useful to those carrying out PASA, if appropriately modified. Analysis of the external agency environment (external forces) and internal value creation processes (“value chains”, “modular assembly” processes or “multi-sided intermediation platforms”) are the most important components of PASA. Also, agency executives must focus on feasible alternatives. In sum, PASA must be practical. But public executives need to take seriously public value, and specifically social efficiency, when engaging in PASA. Unless they do so, their strategic analyses will not have normative legitimacy because enhancing public value is not the same as in some versions of public value or in agency “profit maximization”. Although similarly constrained, normatively appropriate public agency strategic analysis is not “giving clients what they want” or “making the public sector business case”. PASA must be both practical and principled.
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 | 0.066 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.041 | 0.071 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".