A Lesson from Fazal Sheikh’s “Desert Bloom” for Living in a Post-COVID World
Bibliographic record
Abstract
We are told that we can expect to live with an assortment of “new normals” at the end of the COVID-19 pandemic. We are also told that living through the COVID-19 pandemic has made us better able to face longstanding challenges such as climate change and inegalitarian social arrangements. In this paper I reflect on what we are being told by drawing on a lesson I have learned from Fazal Sheikh’s 2011 aerial photographic series to locate evidence of Bedouin villages in the Negev desert in the wake of Israeli campaigns in the 1960s to “make the desert bloom.” The lesson includes recognizing the importance of continuing what Asef Bayat has described as the “silent, patient, protracted and pervasive advancement of ordinary people on the propertied and powerful.” All things considered, I have learned to be distrustful of and resistant to new ways of living that encourage us to learn from past suffering and disasters so as to become ever more resilient and ready for future suffering and disasters in a post-COVID world.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".