Orchestrating Communities, Ubiquities, Time and Space: International Experiences in the Use of Educational Technology
Bibliographic record
Abstract
In this brief introduction we frame the special issue on “Orchestrating communities, ubicuity, time and space: International experiences in the use of educational technology.“ It constitutes the result of the “International experiences in the use of Educational Technology” panel session celebrated within the XXI University Conference on Educational Technology (XXI Jornadas Universitarias de Tecnología Educativa) (JUTE) in Valladolid, Spain in 2013. Every article has gone through a double-blind peer review process with the aim of ensuring not only the quality of the issue but also the adaptation of the initial presentations given in the aforementioned panel session to the rules of scientific publications. This issue brings together five of the works presented in the panel to address a number of relevant challenges in the field of Educational Technology. The topics accomplished by the articles spin around the (mis-)uses of technology in the national accreditation process of teachers in the United States; the tensions derived from the use, re-use and sharing of Open Educational Resources (OER´s) in Europe; an interpretive proposal to orchestrate the evaluation of complex technology-enhanced learning settings, and finally; a experience in the collective generation of documentaries at the Galiano Islands (Canada).
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.041 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".