Understanding the information and communication technology needs of the e‐humanist
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand the needs of humanists with respect to information and communication technology (ICT) in order to prescribe the design of an e‐humanist's workbench. Design/methodology/approach A web‐based survey comprising over 60 questions gathered the following data from 169 humanists: profile of the humanist, use of ICT in teaching, e‐texts, text analysis tools, access to and use of primary and secondary sources, and use of collaboration and communication tools. Findings Humanists conduct varied forms of research and use multiple techniques. They rely on the availability of inexpensive, quality‐controlled e‐texts for their research. The existence of primary sources in digital form influences the type of research conducted. They are unaware of existing tools for conducting text analyses, but expressed a need for better tools. Search engines have replaced the library catalogue as the key access tool for sources. Research continues to be solitary with little collaboration among scholars. Research limitations/implications The results are based on a self‐selected sample of humanists who responded to a web‐based survey. Future research needs to examine the work of the scholar at a more detailed level, preferably through observation and/or interviewing. Practical implications The findings support a five‐part framework that could serve as the basis for the design of an e‐humanist's workbench. Originality/value The paper examines the needs of the humanist, founded on an integration of information science research and humanities computing for a more comprehensive understanding of the humanist at work.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".