Bibliographic record
Abstract
Conveying technically abstract concepts to nontechnical audiences is often a difficult task.It should avoid technical jargon and ensure transmission of meaning with brevity and clarity.Editors Anne Moen, Anders Mrch, and Sami Paavola were confronted with the difficult task of simplifying complex, esoteric technological concepts into simple but meaningful terms to a diverse audience base.They demonstrate an excellent understanding of the content of the articles included in their book and state their goal in the Introduction: to share knowledge that transforms "practices in higher education and in the workplaces" (p. 1).The book is about an ecosystem of various tools, practices, and concepts involved in the process of collaboratively creating knowledge.Practitioners within this ecosystem coined the term trialogical to describe the interactivity between multiple objects, elements, and artifacts in the ecosystem, with the elements in the ecosystem "glued" together by communication and social interaction.The intent is to produce efficiencies through the creation of resources and practices that can be shared and stored for later use.Trialogy refers to a mediatory function performed by an epistemic agent to facilitate interaction between objects of the ecosystem.The term differs from the more familiar word, "dialogic," which refers to bidirectional interactions between objects.To elaborate on trialogy and its relationships with the elements (objects, artifacts, people, and processes), the various authors of the research, who are also practitioners, captured and represented the relationships on a computing platform of three well-integrated tools: the Knowledge Practices Environment (KPE), a graphical collective space for visualizing different knowledge elements and their relationships to each other; the Collaborative Semantic Model (CSM) used to create multiple representations of an idea or phenomenon through the application of modelling languages or algorithms; and the Multimedia Annotation (MA) tool used to annotate multimedia elements, and export, store, and share them for later reuse.This combined platform/trialogic principle forms the basis of the research practice captured in the chapters in this book.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".