The Evolving Role of Information Professionals in Navigating Places, Spaces, and Nurturing New Discourses in the In‐Between
Bibliographic record
Abstract
ABSTRACT Information professionals are at the forefront of navigating the intricacies of shifting landscapes of the Fourth Industrial Revolution and Society 5.0, such as physical places, digital domains, and transitional zones in between, where boundaries blur and new discourses emerge. They are expected to extend their expertise into new domains and enrich their professional practice to better meet the evolving needs of their users. Third Space theory reported in Kuhlthau's work on guided inquiry and information literacy offers a framework, that creates an “in‐between” space, which allows personal experience to merge with professional information and encounters to help information professionals expand beyond their traditional domain expertise. These spaces promote the bridging of theory and practice, the navigation of ethical boundaries, access to multi‐perspective discourse, engagement in active listening, adjustment to evolving technologies, and facilitation of innovative methods. Creating in‐between spaces that promote conversations, interaction, information flow, and access is critical to navigating the intricacies. This interactive panel will explore how information professionals may use Third Space as a framework to offer a new way of thinking and addressing complex societal challenges while prioritizing human values, needs, and well‐being.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.035 | 0.027 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".