Oppressive pines: Uprooting Israeli green colonialism and implanting Palestinian A’wna
Bibliographic record
Abstract
This article provides a comprehensive overview of Israeli green colonialism, denoting the apartheid state’s misappropriation of environmentalism to eliminate the Indigenous people of Palestine and usurp its resources. I focus on the violence of ‘protected areas’, encompassing national parks, forests, and nature reserves. This article argues that Israel primarily establishes them to (1) justify land grab; (2) prevent the return of Palestinian refugees; (3) dehistoricise, Judaise, and Europeanise Palestine, erasing Palestinian identity and suppressing resistance to Israeli oppression; and (4) greenwash its apartheid image. I situate Israeli green colonialism within the broader histories of Western environmentalism – particularly its perpetuation of the human–nature binary – and Zionism. Furthermore, I identify various means through which Palestinians and their land resist this phenomenon. I also explore Palestinian environmentalism, which is influenced by the concepts of a’wna (collaboration), sumud (steadfastness), and a’wda (return), in addition to the Islamic concept of tawhid (unity). I offer it as an alternative environmentalism, which is holistic, anti-racist, feminist, socialist, and nonlinear, while rejecting the trope of the ecological savage. Overall, the intrinsic link between all humans, and them and the environment must be recognised, to realise a just and sustainable society, in Palestine and beyond.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".