Bibliographic record
Abstract
This article compares the concept of the “problem patron” in the library and information science (LIS) and nursing literatures as the basis for developing a new framework for use in LIS. The trend in the LIS literature has been to identify either the patron or the patron’s behavior as the problem. The nursing literature uses interactionist theories to contextualize the so-called problem within a larger framework that includes, among other things, the nurse, hospital-related norms of behavior, the patient care environment, the philosophy of care, and the patient’s own life experiences. This paper examines theories of stigma, deviance, and labeling, among others, as they have been used in the nursing literature to examine the process and effect of labeling. I argue that the work on labeling found in the nursing literature provides the foundation for a new framework to think about the “problem patron” in LIS. In the proposed framework, I define problem behavior at three different levels: the community, the library, and the individual. Using this framework is helpful for thinking about solutions because it encourages us to respond to the “problem” at the level where the behavior is labeled as deviant. This framework is used to explore solutions offered in the LIS literature for the problems that can be identified at each of these different levels.
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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".