Diverse approaches to “tasks” in information science: Conceptual and methodological insights
Bibliographic record
Abstract
Abstract The “task” is an important concept in Information Science, both as a theoretical and methodological tool. While many studies of information retrieval and information seeking and use take individual characteristics or system features as the starting point for their research, a growing body of work has focused on the socio‐cultural perspective. This approach examines the relationship between tasks and search processes, as well as information‐intensive task features and anticipated learning or work outcomes in a given context. This latter perspective has been utilized in the emerging work on collaborative information behavior, which recognizes the interplay of actors, environments, and task demands in understanding information seeking. Building on prior discussions of task‐oriented research, this panel of well‐known and emerging scholars from Australia, North America and Scandinavia will further explore how tasks may guide information seeking and retrieval theory and research. Panelists will present a balance of conceptual investigations, as well as recent empirical studies, to illustrate the wide‐array of issues and insights in this area. Of particular concern to this panel will be the role of diverse perspectives in understanding tasks: how cultural and contextual dimensions of user behavior condition the manner in which we conceptualize tasks, as well as how tasks are utilized in contextually‐sensitive information seeking and retrieval research. The strength of this panel is its diversity: in the spirit of the 2009 ASIS&T Annual Meeting theme, we will explore how, in a pluralistic society, no one presentation on “task” can truly encompass the concept. The topics covered will span life‐long (childhood through adult) as well as life‐wide (formal and informal) contexts of behavior. By bringing together an array of perspectives on this topic, we will foster a wide‐ranging discussion of theoretical and methodological issues surrounding task‐oriented research.
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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 | 0.077 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.030 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.030 | 0.041 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".