A Workflow Analysis Perspective to Scholarly Research Tasks - Auxiliary materials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<strong>These documents contain a number of auxiliary materials for the CHIIR 2020 paper</strong> <strong>A Workflow Analysis Perspective to Scholarly Research Tasks</strong> <strong>Marijn Koolen, Sanna Kumpulainen, Liliana Melgar-Estrada</strong> <strong>Published in: </strong> <strong>Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (CHIIR '20), March 14--18, 2020, Vancouver, BC, Canada.</strong> <strong>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:</strong> <strong>Interview guide for interviewing research project collaborators of the two projects analysed in the paper (see pages 2-3 of this PDF).</strong> <strong>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). </strong> <strong>Full workflow diagrams of both research projects, RP1 (page 7) and RP2 (page8). Please see the paper for descriptions of the projects.</strong> <br>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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 it