Constructing Facts on the Map: The 2020 “Vision for Peace Conceptual Map: The State of Israel and a Future State of Palestine”
Bibliographic record
Abstract
Maps have historically always been intertwined with politics and the making of nation-states. Map-making in Israel/Palestine is a particularly powerful example of the politics of maps. This paper draws on critical cartography, Border Studies and Science and Technology Studies to analyze the “Vision for Peace Conceptual Maps” of a future State of Palestine and the State of Israel that were published by the White House in 2020 as part of a proposed peace plan entitled “Peace to Prosperity: A Vision to Improve the Lives of the Palestinian and Israeli People”. The focus is on the visual rhetoric, discursive underpinnings, and historical context of these maps. The paper also draws on qualitative in-depth interviews as well as academic and policy analyzes of the peace plan’s feasibility and potential impact. While the peace plan and its maps have vanished from the political limelight, they will nevertheless have established “facts on the map” that will embody new spatial possibilities that will inevitable shape imagined futures in Israel/Palestine.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".