Bibliographic record
Abstract
General Chairs MessageThe International Network for Inclusive Technologies (RITIE) has established itself as a working group for the research and development of inclusive technologies in the last five years, starting from an academic collaboration and articulating international collaborations.In these years the annual work of the network is presented at the conference that has been called CONTIE, work that has been published in IEEE Xplore and that this year will continue this level of effort, quality and commitment.This year, 2022, RITIE held the V International Conference on Inclusive Technologies and Education (CONTIE) in San José, Costa Rica, in which 52 articles were received to be evaluated, of which a total of 30 were accepted.The received works represent the effort and commitment from universities in Canada, Costa Rica, Cuba, the United Arab Emirates, Spain, Mexico and Peru.Since its creation, the RITIE and CONTIE network have the great commitment to continue researching and developing inclusive projects that seek to contribute to the improvement of living conditions, particularly the study of people who face some condition of disability.In addition, as the applied research process matures, the challenges of locating funds for the projects are faced and it is pursued that these projects are not only of an academic nature that go beyond publications.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.344 | 0.245 |
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".