Teaching, Designing, and Organizing: Concept Mapping for Librarians
Bibliographic record
Abstract
Concept maps are graphical representations of relationships among concepts that can be an effective tool for teaching, designing, and organizing information in a variety of library settings. First, concept mapping can be used wherever training or formal teaching occurs as a visual aid to explain complex ideas. They can also help learners articulate their understanding of a subject area when they create their own concept maps. When using concept mapping as a teaching tool, students may have a more meaningful learning experience when they add information to a concept map that is based on their current knowledge. Next, concept maps are an effective design tool for librarians who are planning projects. They can also serve as a reference point for project implementation and evaluation. The same is true for the design of courses, presentations, and library workshops. A concept map based on the content of a course, for example, is valuable when selecting learning outcomes and strategies for teaching and assessment. Finally, concept mapping can used as a method for capturing tacit or institutional knowledge through the creation and organization of ideas and resources. Librarians can collaborate on concept maps with each other or with non-librarian colleagues to facilitate communication. Resulting maps can be published online and link to documentation and relevant resources. This paper provides an overview of the literature related to concept mapping in libraries. Concrete applications and examples of concept mapping for teaching and learning, designing, and organizing in library settings are then elaborated. The authors draw from their own success and experience with different concept mapping methods and software programs.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".