Towards cognitive support in knowledge engineering : an adoption-centred customization framework for visual interfaces
Bibliographic record
Abstract
Constructing large knowledge models is a cognitively challenging process.In order to assist people working with these models and tools, this thesis proposes considering the tools used in light of the cognitive support they provide.Cognitive support describes those elements of a tool which aid human reasoning and understanding.This thesis examines the use of advanced visual interfaces to support modelers, and compare some existing solutions to identify commonalities.For many problems, however, I found that such commonalities do not exist, and consequently, tools fail to be adopted because they do not address user needs.To address this, I propose and implement a customizable visualization framework (CVF) which allows domain experts to tailor a tool to their needs.Preliminary validation of this result revealed that while this approach has some promise for hture cognitive support tools in this area, more work is needed analyzing tasks and requirements for working with large knowledge models.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".