Choices in Chaos: Designing Research to Investigate Librarians’ Information Services Improvised During a Variety of Community-Wide Disasters and to Produce Evidence-Based Training Materials for Librarians
Bibliographic record
Abstract
Objective - How can we discover patterns of how librarians develop new information services needed when disaster strikes the community? While there are many guidelines and training materials for planning to protect staff, systems, collections and buildings (in order to return to normal services) in disasters, there are none for quickly improvising needed services. Evidence-based standards and education modules could be very useful to librarians in such crises. Published accounts of such services describe services improvised during a single disaster or during a small number of similar disasters and usually point to the heroic efforts of particular librarians in particular libraries. They tend to be anecdotal and idiosyncratic. The authors needed to design a project using valid research methods to gather consistently and to analyze rigorously narrative data from a wide variety of libraries that have provided improvised services during a wide variety of disasters. Information professionals everywhere strive to provide timely and relevant information in an appropriate format to meet the needs of users. Textbook studies tell us about the value of thoughtful data collection and advance planning before launching new information services for users of libraries and information centers. How can we find out what librarians have done when there is no time for such planning? Method - The authors surveyed a variety of accepted research methods for gathering and analyzing qualitative narrative data describing similar phenomena. They tested some methods in a pilot study of services provided by librarians in southern Louisiana after two hurricanes in 2005. They quickly realized that surveys of hundreds of libraries and interviews of a few librarians did not produce the kind or amount of data to answer the “what” and “how” questions for a variety of libraries in a variety of disasters. They discussed that study and its results with several senior researchers experienced with qualitative methods. (Reports on the pilot study have been published in peer-reviewed publications.) Based on what they had learned during the pilot study and in subsequent discussions, the researchers designed a much larger study to gather evidence of common practice patterns in diverse disasters. Needing to be open to discovery of what happens in different situations they devised a research method based on in-depth interviews, multiple case studies, and narrative data analysis to build grounded theory. The study will conclude with the development of best practices presented as case studies and evidence based training modules for LIS students and practicing librarians. (They submitted the research proposal to the Institute for Museum and Library Services National Leadership Grant program.) Results - The researchers found evidence of the efficacy of Multiple Case Study and Grounded Theory research methods for this kind of for this kind of research question. They developed a protocol to gather data from academic, public, school and special libraries that provided extraordinary services during days and weeks of community disasters caused by earthquakes, massive blackouts, tornadoes, wild fires, hurricanes, land slides, floods, chemical spills and other natural or accidental events. The IMLS agreed with their findings on how to study the question and funded the grant proposal. The researchers have begun the two year project and report briefly on its progress in this paper. Conclusion - Librarians need evidence-based case studies and educational material to learn how to identify needed information services during any kind of community-wide disaster and to respond to these needs creatively. Since this preparation is not currently included in LIS education, standards and guidelines or research literature, there is a need for reliable studies of these phenomena in a variety of libraries and a variety of disasters. The researchers studied and tested various quantitative, qualitative and mixed data gathering methods, tested them, and designed a method for gathering and analyzing the data necessary to support such guidelines and education. Based on their resulting research proposal to study about twenty such phenomena, the Institute for Museum and Library Services has awarded them a two-year National Leadership Grant to perform the study.
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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 | 0.141 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".