PEDAGOGICAL ASSUMPTIONS OF TRANSFORMATIVE DI-GITAL MODEL FOR SOCIAL CHANGE
Bibliographic record
Abstract
Creating of digital models which transform learning and its outcomes, as well as the learner’s educational, developmental and educative achievements has become vital to provision of inclusion possibilities in a networked society. Researchers have produced several frameworks of using digital technologies in education, but these are generally do not appropriately incorporate socio-contextual perspectives. To explore this area and create a transformative model of teaching-learning at higher levels of education in Special pedagogy and social work an appropriate pedagogical provisions are needed to transform educational process as a system including: (a) meaningful and transforming objectives, (b) adequate for digital learning and leading to social change pedagogical principles, (c) reflectivity with the domain of knowledge creation, (d) self-evaluation of learning and social inclusion, (e) transformations of teacher/educator activities towards social inclusion and knowledge share, as well as collaborative learning in organizational settings of emerging knowledge society. This study, focuses on tertiary education and doctoral investigations, reviews the literature on facilitated by transitions social changes, and introduces a theoretical underpinning of digital learning within a pedagogical model. The dominating method is theoretical analysis that includes reviewing, analysing and synthesising literature on the theme “in an integrated way such that new frameworks and perspectives on the topic are generated” (Torraco, 2005, p. 356; Hamilton & Torraco, 2013). The article introduces the theoretical approach to the project “Implementation of Transformative Digital Learning in Doctoral Program of Pedagogical Science in Latvia” and “Gender aspects of digital readiness and development of human capital in region”.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".