Geographic Information Systems (GIS) in public administrationc An introduction to a series of articles
Bibliographic record
Abstract
Geographic Information Systems (GIS) represent a promising generation of powerful tools developed with the help of both traditional and new Informationand Communications Technologies (ICTs) in Public Administration (e.g., new database technologies, the Internet). Indeed, while discussing the generic applications that every public administrator should master, one commentator explicitly states that the development and use of GISs is on: “(. . . ) the fast track to becoming such a generic application in public administration” [1,3]. Since their first appearance in public administration, GISs have been expected to contribute to a more comprehensive, technocratic and rational mode of policymaking. Some authors (as Carver c.s. in this issue) expect them to enable more effective democratic participation in these policies. Others are pointing at the way these systems, contrary to some widely-held beliefs about the “deterritorializing”-capacities of ICTs in public administration, are causing a re-assessment of the notion of territory in public administration (e.g., Snellen, this issue, the article of Lips c.s., next issue). More rationality, democracy, or territoriality; the development and use of GISs in the public domain is a new technology that, again, provokes a re-examination of traditional notions and values in public administration [4]. In the forthcoming issues of Information Infrastructure and Policy, we present a series of articles that explore, discuss and reflect these intriguing claims as well as specific questions that are raised about the development and use of GISs in the public domain. Some of these questions originate in the work of the permanent study group on informatization in public administration of the European Group for Public Administration (EGPA – Annual Conference in Paris, France, 1998). Others are written especially for this series. In this issue are published, the articles of Snellen (The Netherlands), Carver c.s. (United Kingdom) and Sahay and Albuquerque de Borges (Canada and Brazil). In the next issue, two others will follow.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".