Bibliographic record
Abstract
Objective: The impetus for this project is to begin to understand open science practices and obstacles at the XXX. This project uses open-ended questions to understand the ways in which university-affiliated individuals learn about, think about, and interact with open science. The goal of this study is to showcase the complexity and diversity of activity and challenges in this domain to help determine how best to move open science forward. Methods: From March to October 2022, 45 semi-structured interviews were conducted with faculty, graduate students, librarians and administrative staff. Interviews were conducted and recorded using Zoom and the audio was transcribed using otter.ai. As part of a commitment to open science practices, a data management plan was created and with participant consent, 26 transcripts were uploaded to Dataverse. Data analysis used structured coding and thematic development to investigate responses. Results: The core finding of this study is that there is no singular status of open science at XXX. The qualitative findings reflect a diversity of opinions, practices and relationships to open science. Conclusion: For open science practices and scholarship to have longevity, there must be systemic changes to adopt more open activities. XXX is well positioned to guide the transition and harness open principles to move into the future.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | low |
| gpt | Open science Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: yes | Not applicable | low |
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.109 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".