Conceptualizing information work for health contexts in Library and Information Science
Bibliographic record
Abstract
Purpose The purpose of this paper is to suggest that a closer consideration of the notion of work and, more specifically, information work as a sensitizing concept in Library and Information Science (LIS) can offer a helpful way to differently consider how people interact and engage with information and can complement a parallel focus on practices, behaviours and activities. Design/methodology/approach Starting with the advent of the concept of information work in Corbin and Strauss’ work, the paper then summarizes how information work has evolved and taken shape in LIS research and discourse, both within and outside of health-related information contexts. Findings The paper argues that information work affords a lens that can acknowledge the multiple levels of effort and multiple processes (cognitive, physical or social-behavioural) related to information activities. This paper outlines six affordances that the use of information work within LIS scholarship imparts: acknowledges the conceptual, mental and affective; brings attention to the invisibility of particular information activities and their constituents; opens up and distinguishes the many different lines of work; destabilizes hierarchies between professionals and non-professionals; emphasizes goals relating to information activities and their underlying pursuits; and questions work/non-work dichotomies established in existing LIS models. Originality/value This paper is a first in bringing together the many iterations of information work research in LIS. In doing so, this paper serves as a prompt for other LIS scholars to take up, challenge the existing borders of, and thus advance the concept of information work.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.013 | 0.082 |
| Scholarly communication | 0.031 | 0.025 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 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".