A Workflow Analysis Perspective to Scholarly Research Tasks - Auxiliary materials
Bibliographic record
Abstract
These documents contain a number of auxiliary materials for the CHIIR 2020 paper A Workflow Analysis Perspective to Scholarly Research Tasks Marijn Koolen, Sanna Kumpulainen, Liliana Melgar-Estrada Published in: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (CHIIR '20), March 14--18, 2020, Vancouver, BC, Canada. The paper analyses the workflows of two research projects in the domain of Digital Humanities. The analysis is based on coded interviews for two project collaborators for each project. There are three types of auxiliary materials: Interview guide for interviewing research project collaborators of the two projects analysed in the paper (see pages 2-3 of this PDF). Codebook for coding research activities of the analysed research projects. This is based on the NeDiMAH Methods Ontology (NeMO), extended with a few activities that have no equivalent in NeMO (pages 4-6). Full workflow diagrams of both research projects, RP1 (page 7) and RP2 (page8). Please see the paper for descriptions of the projects.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".