Making Narrative Count: A Narratological Approach to Public Management Innovation
Bibliographic record
Abstract
Though the use of narrative has become widespread through many disciplines, it has yet to establish a strong footing in public administration. The article first explains why narrative analysis has not been incorporated into mainstream public administration as the latter has become increasingly empirical, quantitative, and hypothesis driven. The article then outlines a number of key narratological concepts that could readily be applied to the field. Demonstrating the possibilities they offer, the concepts are applied to the analysis of the 31 finalists in the 2008 and 2009 Innovations in American Government Awards to identify a dominant innovation fable incorporating incremental problem-solving and interorganizational cooperation. Because the Awards application process results in three distinct narratives—a detailed article application, a site visit report, and an oral presentation to the selection panel—the analysis focuses on the differences among them, with the application form representing an insider's story written by experts for an expert audience, the site visit report often incorporating a counter-narrative that points out the innovation's unresolved conflicts or uncertainties, and the oral presentation functioning as an advocacy narrative directed at a generalist audience. The article concludes with suggestions for further narratological research about public management innovation, taking advantage of the new application form to the Innovation Awards that was designed to elicit more explicit narratives. More generally, it raises possibilities for public administration scholars to incorporate narratological concepts and methods into their research. Aunque el uso de narrativa se ha esparcido en muchas disciplinas, está aún necesita establecerse en el área de la administración pública. El artículo primero explica porque el análisis narrativo no ha sido incorporado en estudios convencionales de administración pública dado que este último se ha centrado en métodos empíricos y cuantitativos guiados por hipótesis. El artículo luego esboza varios conceptos claves narratológicos que podrían ser usados fácilmente en la disciplina. Para mostrar las posibilidades que estos conceptos ofrecen, los conceptos son aplicados a un análisis de los 31 finalistas en los Premios del Gobierno Estadounidense en los años 2008 y 2009 para identificar una fábula dominante de innovación que incorpore resolución incremental de problemas y la cooperación entre organizaciones. Como el proceso de aplicar a los Premios resulta en tres narrativas distintas---un artículo aplicado y detallado, una visita a la organización participante y una presentación oral ante el jurado---el análisis se centra en las diferencias entre ellas. El artículo aplicado y detallado representa el cuento de una persona muy bien informada, escrito por expertos para un público de expertos. El trabajo sobre la visita a la organización muchas veces incorpora una narrativa en contra de los conflictos y las incertidumbres no resueltas. La presentación oral sirve como narrativa que aboca y es dirigida a un público general. El artículo concluye con algunas sugerencias para más investigaciones narratológicas sobre la innovación en la administración pública, usando la nueva solicitud de Premios de la Innovación que fue diseñada para obtener narrativas más claras. Desde una perspectiva más general, el artículo también ofrece las posibilidades para que los investigadores en el área de la administración pública incluyan los conceptos y métodos narratológicos en sus estudios. Translations by Claudia N. Avellaneda, University of North Carolina Charlotte and Nicolai Petrovsky, University of Kentucky
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".