Quality, Equity, Inclusion and Lifelong Learning in Pre-service Teacher Education
Bibliographic record
Abstract
Abstract Sustainable Development Goal 4 (SDG 4) focuses on ensuring inclusive and equitable, quality education and lifelong learning opportunities for all. One of the three means of implementation of SDG 4 targets is SDG 4c which calls on countries and donors to significantly increase the supply of qualified teachers in developing and underdeveloped countries. This emphasis on the supply of teachers is in recognition of the fact that the quality of education ultimately depends on teachers. In general, there is widespread agreement that teacher education has an important role to play in the achievement of the SDG 4 targets. However, there has been limited attention in the literature to SDG 4 in a context of teacher education. This paper aims to contribute to the literature on SDG 4 and teacher education. The paper first presents a conceptual framework pertaining to quality, equity, inclusion, and lifelong learning. Next, the framework is applied to the case of Thailand to identify examples of progress the country is making in support of realization of SDG 4 in teacher education. The framework with the four concepts can be applied by researchers to identify examples of progress on SDG 4 in teacher education in other countries and contexts.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".