Bibliographic record
Abstract
Purpose Institutional ethnography is a method of inquiry that brings attention to people’s everyday work while simultaneously highlighting broader sites of administration and governance that may be organising that work. The purpose of this paper is to argue that the integration of institutional ethnography in health information practice research represents an important shift in the way that Library and Information Science professionals and researchers study and understand these practices. Design/methodology/approach This paper first explores the key tenets and conceptual underpinnings of Dorothy Smith’s institutional ethnography, illuminating the importance of moving between translocal and the local contexts and identifying ruling relations. Drawing from a library and information science study that combined interviews and textual analyses to examine the social organisation of family caregivers’ health-related information work, the paper then explores the affordances of starting in the local particularities and then moving outwards to the translocal. Findings The paper concludes with an overall assessment of what institutional ethnography can contribute to investigations of health information practices. By pushing from the local to the translocal, institutional ethnography enables a questioning of existing library and information science conceptualisations of context and of reappraising the everyday-life information seeking work/non-work dichotomy. Ultimately, in considering both the local and the translocal, institutional ethnography casts a wider net on understanding individuals’ health information practices. Originality/value With only two retrieved studies that combine institutional ethnography with the study of health information practices, this paper offers health information practice researchers a new method of inquiry in which to reframe the application of methods used.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| 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".