Bibliographic record
Abstract
This article probes the ways in which returning to Palestine is imagined in Razan AlSalah’s two video works Your Father Was Born 100 Years Old, and So Was the Nakba (2018) and Canada Park (2020). In foregrounding the refusal of configurations substantiated by state concessions and normalisation treaties, the article treats loss as central to the manifold rehearsals of return. In AlSalah’s work, loss is understood not as becoming less, but rather as a proposition for becoming otherwise. Here, the practice of loss is explored through the glitch as both a conceptual framework espousing opacity and a pragmatic tool engaging pixel breaks. Rather than reducing the glitch to a mere erroneous aesthetic, the article underscores the active capacity in encountering a glitch or deliberately engendering it by exploring the tensions between colonial imageries reproduced in digital maps, and, in contrast, montage as AlSalah’s tool for intervention. Finally, the article serves as a theoretical experimentation with what I call “dialectical poethics,” which reads the filmic return through loss as an attempt to go against linearity, intelligibility, and finality, yet insists on a materialist grounding of the glitch as a method that is historically situated rather than always-already emancipatory.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".