Replication Data for: Digital Tools and Techniques in Scholarship and Pedagogy in the Social Sciences and Humanities
Bibliographic record
Abstract
Note: this dataset is replication data for the paper: Kampen, A., Pearson, M., & Smit, M. (2016). Digital Tools and Techniques in Scholarship and Pedagogy in the Social Sciences and Humanities. Technical Report, Dalhousie University. This data is about the adoption, diffusion and use of digital tools and techniques within the social sciences and humanities research communities (Kampen, Pearson & Smit, 2016). The dataset includes a weighted random sampling of 1001 articles (500 from the Social Sciences subject area; 501 from the Arts and Humanities subject area) from original research articles published in academic journals in 2014. These articles were assessed individually for the presence, and nature, of digital tools and techniques used in the research process, particularly collection, analysis, and/or visualization. Each article was also assessed to determine if it belonged to one of the three focus areas identified by the funding agency: Diversity/Inequality/Differences, Environmental studies, and Resilient and innovative societies. Tabular data was recorded based on these assessments; a complete key is included in the Readme file. The tabular data and the bibliography for the sampled articles are included. Data consists of: 1) One tabular data file (CSV) containing, for each citation: - Numeric citation identifier - Metadata (e.g., title, subject areas, granting information) - Digital tools analysis (e.g., Type of digital tool used, name of digital tool, source of digital tool) - Research notes 2) One BibTeX file containing citations for the 1001 articles analyzed (citation identifier matched to CSV id) 3) One PDF file containing the output of the BibTeX file 4) One Readme file containing data description, cleaning techniques, and known remaining issues
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.
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 | 0.032 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.212 | 0.107 |
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".