Mapping the Middle East: Israeli Student’s Geographical Knowledge and Perceptions
Bibliographic record
Abstract
In 1959, Roderic Davison asked “where is the Middle East”, a query still lacking a common definition or perception. Our study investigates “where is the Middle East” of young Jewish-Israeli students, while assessing their geographic awareness and perceptions regarding this region. We surveyed 262 students in 2016-2017 and 2021, asking them to identify Middle Eastern countries on an outline map. Our data analysis considers the Israeli formal geography education and the Israeli media exposure to the Middle East as potential sources of knowledge, along with known spatial-mental factors found in other mental map studies. Overall, the students exhibited a medium-level knowledge of the political map (49% correct identifications) and a strong knowledge of neighboring countries (86% accuracy), as their mental maps centered around Israel. The findings unveiled a common understanding of the region, evident in recurring familiarities and mistakes. Surprisingly, their maps did not reveal significant segregation perceptions of Israel the Middle East. This research sheds light on Israel's connection to the Middle East, as reflected in its education system, media, and the mental maps of its youth.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".