Bibliographic record
Abstract
As it happened in 2014, when we published some selected papers from the OpenCourseWare Consortium Global Conference (vol. 6 issue 2), this special issue is the result of an agreement between ICDE and the Open Education Consortium and a collaboration with Open Praxis to feature, in the journal, research and innovative practice presented at the Open Education Global Conference 2015, held in Banff (Canada) in April 22-24.The selected papers presented in this issue are a good example of some trends we currently find in the field of open education:• The increasing number of research and showcases with a focus on openness, providing a rigorous basis for getting recommendations, lessons learned, highlights.• The relevance of open educational practices, a step beyond open educational resources and open courses.• The core position of open education within the transformation of higher education, and the relevance of institutional strategies which include "open" to do so.As stated in the website (http://conference.oeconsortium.org/2015/about-conference/), the Open Education Global Conference is the annual opportunity for researchers, practitioners, policy makers and educators to deeply explore open education and its impact on global education.Conference participants will hear from thought leaders in open education and have the opportunity to share ideas, practices and discuss issues important to the future of education worldwide.Sessions cover new developments in open education, research results, innovative technology, policy development and implementation, and practical solutions to challenges facing education around the world.The convergence of these topics with those of interest for Open Praxis has facilitated the partnership for the preparation of this special issue.The conference features the following tracks:Pedagogy & Design: Highlights the design and delivery of open educational resources, use of OER, Open Access journals and open data, open MOOCs, learning science, instructional design, remixing resources and teacher training.According to the distribution of the conference presentations, three of the published papers relate to this strand.• Innovation: Presents new and exciting developments in open education, big data and its use in education, new lenses for considering open education, educational trends, new technologies that enable learning, assessment and accreditation in open education.One paper is ascribed to this track.• Evidence of Impact: Discusses research on all aspects of open education, including learner success, usage data, indicators of awareness, effectiveness of open policies, workforce development, and economic considerations.• Strategy: Includes policy development and considerations, working with policy makers at institutional, governmental and intergovernmental levels, developing an institutional strategy, business models, and cooperative efforts and funding.One paper relates to this topic.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.164 | 0.091 |
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".