Challenging Collaborative Consumption at a Critical Juncture: Airbnb in the Matrix of Gentrification and Colonization
Bibliographic record
Abstract
Ten years since the nationwide J14 housing protests against Israel’s increasing cost of living, affordable housing remains just as scarce, even prompting some city-dwellers to seek cheaper living in West Bank settlements – a military-occupied Palestinian Territory. Given that Israel is often called the Start-Up Nation, Airbnb’s rise in Tel Aviv-Yaffo and West Bank settlements was exceptional. Thus, my thesis aims to determine Airbnb’s role in the Tel Aviv-Yaffo housing crisis and the West Bank settlement economy. My research demonstrates the ways in which Airbnb accelerates the existing historic trends in Tel Aviv-Yaffo and the West Bank by introducing new capital flows via the sharing platforms. \nWhile existing Israeli literature on Airbnb’s activity in Tel Aviv-Yaffo has done an adequate job of addressing the phenomenon as a policy and legal issue, none have understood it within Israel-Palestine’s gentrification-colonization matrix. My contribution is applying Anglo gentrification literature to consider how these activities unfold in particular ways within Israel-Palestine’s unique colonial context. Through a post-colonial lens, my thesis reveals how Airbnb has exacerbated the housing crisis by converting residential units into illegal hotels, displaced marginalized Jews in south Tel Aviv-Yaffo, turned human rights violations into tourist attractions, and further displaced Palestinian Arabs living both within and outside the Green Line.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.039 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".