Learning through Upheaval: Strategies for Analyzing and Construing Emerging Sociopolitical Transformations in the Middle East
Bibliographic record
Abstract
Abstract The irreducible complexity and singular unpredictability of the upheavals that have roiled the Middle East since December 2010 challenge analysts—from university students to policymakers—to grapple with irresolvable questions; this, rather than analysts' superimposing their own visions of what might constitute the upheavals' driving forces, and what will, or should be the outcomes of the regional turmoil. Drawing on strategies gleaned from teaching about the Arab uprisings, this article asserts that the uprisings may be collectively read as comprising a text that contains signs of indeterminacy pointing to many possible meanings and sources of meanings. Focus is placed on those signs that embody the differing discourses through which the Middle East upheavals are, have been, or can be represented and assessed; and the fluid, multidimensional forms of political identity that have contributed to the upheavals, and are being further reshaped, in their wake. By reading these signs with intellectual openness and humility, interpreters can achieve greater insight into the profoundly contingent and unforeseeable dynamics at work across the region.
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 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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".