A realistic researcher’s take on Open Science services
Bibliographic record
Abstract
Katie Smart served as a research librarian at UiT The Arctic University of Norway from 2022 to 2025. A geologist specializing in research on mantle petrology, she has experience from three countries (Canada, Germany, South Africa) before she arrived at UiT. In this episode, she discusses different local and national services for open science that she has been involved in during her time in Norway and emphasizes that marketing open science to academia must include the perspective of the target audience: the researchers. Understanding the academic mindset and catering to researchers’ needs is key for success in widespread adoption of open science practices, she argues. Words matter: do not use the jargon of librarians and other service staff but find expressions that trigger researchers’ interest. Do not take for granted that researchers are idealists willing to change habits just for the sake of the common good. Although open science can be framed benefiting academia and society as a whole, in order to get strong buy-in from academia it must also be framed as to how it will propel each individual’s career. First published online: July 4, 2025.
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 | 0.043 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.018 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".