Bibliographic record
Abstract
ABSTRACT This research challenges dominant understandings of ubiquity, mobility, and connectivity and explores the limits ICTs through a qualitative study of a collaborative capacity‐building initiative to localize the repair of medical devices and equipment in the Gaza Strip. Dominant perceptions of ICT affordances rely upon taken‐for‐granted political, economic, and social systems that are neither universal nor guaranteed. Using a thickly descriptive, interpretivist approach, this research shows how ICTs are fundamentally insufficient to support team collaboration and meet the affective and material requisites of collaborative work under conditions of occupation. Digital networked technologies are particularly limited in their ability to create, simulate, and/or foster the interdependent conditions of presence, flow, and coordination required for cooperative work to succeed. Geopolitical borders and concomitant conditions of occupation continuously disrupt the logics of time and space between those living and working in Gaza and the “outside world”. Arbitrary and capricious fluctuations in tolerance, temporality, persistence, and permeability wrought by the ongoing siege of Gaza result in pernicious harms that are difficult or impossible to account for or correct with technical “solutions.”
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".