Improving Education for a More Equitable World: Social Justice Perspectives
Bibliographic record
Abstract
•This paper delves into the reports, core discussions and outcomes of the 67th Annual Conference of the Comparative and International Education Society (CIES) held in a hybrid format in Washington, D.C. in 2023, focused on the theme “Improving Education for a More Equitable World.”•In response to the CIES 2023 theme, Written Responses from global scholars, along with four special papers inspired by the Kneller Lecture and Keynote Speeches, have been published across four distinguished journals. This paper feature two Written Responses and one Keynote Speech, sparking discourse on enhancing education for a more equitable world.•The first article is a keynote speech highlighting the crucial role of well-prepared teachers, stressing their absence perpetuates inadequate learning cycles.•The next article underscores the shared responsibility of governments, businesses, and NGOs in steering educational reform toward a more inclusive and equitable future.•The final paper underscores the importance of tackling inequity at its core by embracing the transformative power of interconnected love, oneness, empathy for a truly equitable world.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.007 | 0.011 |
| 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".