The Landscape of Digital Technology to Enhance the Digital Researcher
Bibliographic record
Abstract
The objectives of this research were to synthesize the competencies of the digital researcher, carry out an empirical investigation of the digital researcher landscape, and evaluate the results of a synthesis of digital researcher competency. To conduct the research, the researchers carried out a review of the literature related to researcher competency, digital competency, digital researcher competency and digital technology for researchers. Then, a focus group discussed the conclusion of the digital technology landscape used to enhance the digital researcher. The results showed that digital researchers’ competency had six features: 1) Personalize and Security Competency, 2) Literature Review and Reference Management Competency, 3) Communication and Collaboration Management Competency, 4) Analyzing and Reporting Competency, 5) Proofreading and Plagiarism Checking Competency, and 6) Publication Competency.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| 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".