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Record W3014744207 · doi:10.1007/978-94-6209-590-8

October Birds: A Novel about Pandemic Influenza, Infection Control, and First Responders

2014· book· en· W3014744207 on OpenAlexaboutno aff
Jessica Smartt Gullion

Bibliographic record

VenueDIAL (Catholic University of Leuven) · 2014
Typebook
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicVirologyInfluenza pandemicMedicineBiologyCoronavirus disease 2019 (COVID-19)Internal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

En route to a conference, a physician from Jakarta boards a plane to the US. He does not know he is the index patient for the next global influenza pandemic. From this catalyst, thousands of people will get sick, hundreds of people will die. October Birds follows the healthcare and emergency management responders in the town of Dalton, Texas as they cope with the unfolding pandemic. Dr. Eliza Gordon, Chief Epidemiologist for the city struggles to control the outbreak and be a mother. Infectious disease specialist Dr. Ben Cromwell tries to maintain control of the increasing numbers of patients at Memorial Hospital, while Memorial's infection control specialist fights to limit the spread of the disease to the healthcare workers and the other patients. Dalton's emergency manager copes with an ever increasing logistical nightmare, and the incident commander tries to hold everything together. Meanwhile a currendera in the town searches for a cure. October Birds is grounded in real-life public health practice, sociological research, and emergency management. It is ‘a/r/tographical research,’ sociological inquiry within the science/art intersection. October Birds is more than a story – it is also a sociological theory of community-level response to health threats. This novel can be read as a supplementary text in a number of disciplines, including sociology, nursing, public health, health studies, emergency management, and psychology, and can be used in qualitative research methods courses as an example of arts-based research. I hope it will also be read simply for pleasure, and instill the question: ‘What if?’ What if a devastating pandemic does emerge? How will we respond? Social Fictions Series Editorial Advisory Board Carl Bagley, University of Durham, UK Anna Banks, University of Idaho, USA Carolyn Ellis, University of South Florida, USA Rita Irwin, University of British Columbia, Canada J. Gary Knowles, University of Toronto, Canada Laurel Richardson, The Ohio State University (Emeritus), USA Jessica Smartt Gullion, PhD, is Assistant Professor of Sociology at Texas Woman’s University, where she teaches courses on medical sociology and qualitative research methods. Dr Gullion is the author of more than twenty peer-reviewed articles, in such journals as the International Review of Qualitative Research, the Journal of Applied Social Science, Qualitative Inquiry, Infection Control and Hospital Epidemiology, the Archives of Internal Medicine, and Clinical Infectious Diseases. Her research focuses on how communities cope with health threats.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.273
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

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