Foreword: Inclusion Education: a Burning Issue for Researchers around the World
Bibliographic record
Abstract
On January 16 and 17, 2020 was held in Lebanon, the 1st edition of the international REACH conference organized in partnership between the NGO T.I.E.S (Together for Inclusive Educational Systems) and the American University of Beirut. Meeting under the acronym Research for Education Accessibility Challenges, 31 participants from Lebanon, Jordan, France and Canada wished to contribute to open wider the doors to education for all in an inclusive aim. The crisis prevailing in many countries, and amplified by the COVID-19 requires education systems to be rethought so that they no longer contribute to maintaining or even increasing the inequalities already present in our societies. In this context, scientific research on the implementation of inclusive education is crucial for the improvement of our education systems.
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 | 0.017 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.043 | 0.042 |
| Insufficient payload (model declined to judge) | 0.040 | 0.046 |
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".