Bibliographic record
Abstract
This research work is intended to assess the usability of Pictogram symbols and other visual symbols in an audio-visual strategy to facilitate and enhance the use and learning of English as an additional language for Arabic-speaking Syrian refugees, with a potential for generalizing the process to speakers from other linguistic backgrounds. The adopted software for the project is PICTOPAGES, a versatile tool with 2,200 symbols, 78 animated symbols, and the potential for customization with photographs, thus augmenting its capability for personalization and relevance. While PICTOPAGES is the intended basis for this research, the concept and software will be adapted and modified as may be required. PICTOPAGES includes text, recorded speech, and symbols and is currently available for iPad. In the future, it may be adapted for use on iPhone. A preliminary design using PICTOPAGES has been created for this research. The focus group includes, but is not limited to, newcomers who may have limited to no English skills, limited resources, limited education, and potentially limited literacy in their native language, and perhaps high levels of distraction and frustration related to their recent experiences. Enhanced communication capability and confidence should enhance the participants employment potential. Extensive interaction with respect to communication requirements, selection or development of readily understandable symbols, and real-world testing would be undertaken with an intended user group. A potential subset of the focus group could involve members of the refugee community that, in addition to English language limitations, also have developmental or acquired disabilities that affect their ability to communicate verbally (per the original intent of the software).
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".