IMPAKT Open Science: Initiative to Measure Perceptions, Attitudes, and Knowledge abouT Open Science // IMPACT Science Ouverte: Initiative pour Mesurer les Perceptions, les Attitudes et les Connaissances en maTière de Science Ouverte
Bibliographic record
Abstract
Open science is the practice of making scientific inputs, processes, and results freely available to all, with minimal barriers. Globally, open science is driving a dramatic shift in research culture, and more and more governments and research organizations are adhering to the conceptual principles of open science. However, the lack of empirical evidence on what people think about open science, how they feel about it, and to what extent they apply open science practices in their daily workflows severely limits the practical implementation of open science. With limited empirical evidence, governments and funding agencies face significant difficulties in designing community-tailored open science infrastructures, training opportunities, incentives, and policies. The overarching objective of IMPAKT Open Science is to measure and describe researchers’ practical adoption of open science, and determine factors that predict its uptake, including researchers’ perceptions, attitudes, and knowledge about open science. We will pursue this objective by applying and validating a novel self-report assessment instrument, developed by our research team. This instrument will allow us to identify, qualitatively describe, and quantify the perceptions, attitudes, and knowledge about open science of different stakeholders at diverse academic levels (i.e., faculty members, research staff, postdoctoral fellows, graduate students and undergraduate students) working in different academic institutions in Québec and the rest of Canada. In addition, we will conduct one-on-one interviews and focus-group sessions, which will contribute to generating rich qualitative data that will add nuance and richness to quantitative data. We hypothesize that (i) knowledge is the primary factor motivating the implementation of open science practices, (ii) attitudes mediate the relationship between knowledge and implementation of open science, and (iii) perceptions about open science moderate the effect of knowledge and attitudes. Our study will adopt a mixed-methods approach to comprehensively explore participants' perceptions, attitudes, and knowledge about open science. Qualitative analysis will focus on linguistic data from in-depth interviews, focus groups, and open-ended survey questions. Open, axial, and selecting coding will allow us to identify and relate categories emerging from participants’ discourse. Quantitative data resulting from the survey will be analyzed by calculating descriptive statistics, applying exploratory factor analysis to investigate latent factors, and fitting mixed-effects statistical models to assess relationships between the different dimensions of the survey and test hypotheses. IMPAKT Open Science will allow for a more comprehensive understanding of the factors currently driving the implementation of open science practices. Beyond furthering our understanding of open science, our project has important implications for knowledge translation. Specifically, findings will have the potential to inform evidence-based policymaking.
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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 | 0.048 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".