Bibliographic record
Abstract
Although information searching is one of the most popular online activities people engage in for a variety of goals and tasks every day, search systems have long been viewed from a rather limited perspective. That is, search systems have been typically viewed as tools for retrieving online content to satisfy information needs. However, today’s search systems support people’s interactions with information and help people access and use information in ways that go beyond offering a set of search results for specified search tasks. Despite the fact that information search systems have evolved from information-retrieval tools to full-text information-intensive systems over the past two decades, researchers have only recently started recognizing search systems as rich online spaces in which people can learn and discover new knowledge while interacting with online content. This does not mean that searching and learning have not been seen as connected in the field of information science. In fact, there have been numerous studies on the intersection between searching and learning. However, the association between searching and learning has often been defined in terms of searching in the learning environment, having learning as a search goal or learning about searching, focusing on teaching search and evaluation skills to youth. As a result, the concept of learning has often been assumed rather than clearly being articulated in most information science studies. A new research direction we present in this special issue is ‘Searching as Learning’, which attempts to move away from rather simplistic conceptualizations either as searching to learn or learning to search. From the perspective of searching as learning, we propose to reconsider the value of search systems in supporting human learning directly while focusing on the impact, influence and outcomes of using search systems with respect to a learning process. We believe that there are great opportunities to leverage and extend current search systems to foster learning by reconfiguring search systems from information-retrieval tools to rich learning spaces in which search experiences and learning experiences are intertwined and even synergized. The idea of studying and designing search systems to foster learning during the search process and create a rich learning space has been attracting growing recognition among researchers and practitioners in recent years. This Special Issue is a follow-up to the Searching as Learning (SAL 2014) workshop ( held in conjunction with the Information Interaction in Context (IIiX) Confe
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.248 | 0.146 |
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".