A Market-Segment Template for Public-Sector Organizations: Introducing a Client Stance Model
Bibliographic record
Abstract
ABSTRACTWhat can government and nonprofit organizations do when different segments of the public respond differently to the same program? How can these programs operate efficiently - and effectively - with all segments of the target population? After all, public-sector organizations are often criticized for blind uniformity in dealing with the public. This discussion paper offers an intuitive, practical approach for defining important differences among population segments based on attitudes and behavior. Then, given this segmentation, it sets the stage for developing administrative and regulatory enforcement strategies that ease compliance and, when necessary, trigger sanctions for noncompliance.Keywords. Market segmentation, public-sector clients, public sector innovationIntroductionWhat can government and nonprofit organizations do when different segments of the public respond differently to the same program? How can these programs operate efficiently - and effectively - with all segments of the target population? After all, public-sector organizations are often criticized (Dvorin and Simmons, 1972; Goodsell, 2004) for blind uniformity in dealing with the public. This discussion paper offers an intuitive, practical approach, first, for defining important differences among population segments and, second, for developing administrative and regulatory enforcement strategies that allow ease of compliance and, when necessary, sanctions for noncompliance.Definitions and ContextFor purposes of this study, these terms carry the following definitions:Market segmentation - The practice of dividing markets up into homogenous 'segments' of consumers or customers. The members of any given segment are assumed to respond to communication or to behave in the same way (Barnett and Mahoney, 2011, p. 9).Program administrators - Nonprofit and government officials or organizations responsible for public-sector undertakings including such activities as enforcing regulations and providing public services. For convenience in this paper, these are frequently referred to as programs.Program clients - The targeted population of individuals or organizations subject to a public-sector program or other activity. The relationship may be voluntary (such as a city park user) or involuntary (such as a regulated business) and may range in frequency from occasional to continual (Hyde, 1992).Public sector - Governmental and nonprofit entities. Includes levels of government, bureaus and agencies, and other organizational labels. An organization can be a part of the public sector or the private sector; however, this paper focuses on organizations in the public sector.These terms and variants of these terms are used throughout this paper.Public-sector organizations encompass governmental entities and nonprofit entities. Their goal, broadly speaking, is to serve the public at large. Therein, they differ from private-sector organizations, which seek foremost to benefit their owners. But public and private organizations do their jobs by addressing a defined need through products and services - and through attention to target populations such as veterans, drivers, Medicare users, or residents of a given community.Public-sector organizations differ from one another in size, complexity, and purpose. Nonprofit organizations might be set up to serve their own members (as with a labor union, homeowners association, or professional society) or to reach external clienteles (as with a hospice service, a philanthropic foundation, or an advocacy group). Government organizations might be set up to create and maintain infrastructure or provide public services (as with public works departments, emergency response, or school districts) or to assure compliance with a group of laws and regulations (as with workplace safety, insurance and banking, or the courts). These institutions serve us all by their focus on a particular mission and, almost always, on a target population. …
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".