Bibliographic record
Abstract
The Program “Educational and pedagogical paradigm shift in theory and practice” is implemented in Applied Research Implementation of Transformative Digital Learning in the Doctoral Study Program “Pedagogy” in Latvia (DocTDLL). This program is developed in cooperation with University of Ontario, Institute of Technology, Canada (EILAB) which is appropriate for implementation in higher education of Latvia and help to develop theoretical basis and practices of tertiary pedagogy. Transformative Digital Learning (TDL) transforms higher educational practices from: authoritarian instruction to democratized learning, individualistic learning to socially distributed and tool-mediated, knowledge building, a mechanism of the status quo to a catalyst for social progress, offer a truly novel approach to the theory of teaching and education, introduce new methodology and practical techniques to learners of all ages. The discourse changes concepts of education because technologies change the learner, ‘what how’ of this process, democratizes learning, communication, cooperation, interpersonal relationships and emphasizes the presence of moral values in education (Mykhailenko, Blayone, Žogla Ļubkina, 2019); Žogla, Prudņikova Mykhailenko,2019; Žogla, Ušča, Kijaško,2019).The purpose of the article is to analyse main results of approbation for the Program “Educational and pedagogical paradigm shift in theory and practice” for further investigation by the above mentioned project.
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 | 0.050 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".