A qualitative study of social sciences faculty research workflows
Bibliographic record
Abstract
Purpose This paper is a qualitative study of how social sciences faculty construct their research workflows with the help of technological tools. The purpose of this study is to examine faculty scholarly workflows and how both tools and practices support the research process. This paper could inform academic libraries on how to support scholars throughout the research process. Design/methodology/approach This is a qualitative study case study of ten faculty members from six research universities from the United States and Canada. Semi-structured interviews were conducted and recorded. Atlas.ti was used to code and analyze the transcripts; each participant was a separate case. Descriptive coding was used to identify digital tools used for collaboration; process and descriptive coding was utilized to examine practices in scholarly workflows. Findings Through case study analysis the results of this study include the role of technology in faculty research workflows. Each workflow was grouped into four categories: information literacy, information management, knowledge management, and scholarly communication. The findings included scholars creating simple workflows for efficiency and collaboration and utilizing workarounds. Research limitations/implications The study did not observe faculty in the process of doing research and, thus, only reports on what the researchers say that they do. Originality/value The research is unique in that there is almost no research on how social scientists conduct their research workflows and the affordances/impasses of this process.
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".