Bibliographic record
Abstract
T he Free Gaza movement began in 2006 as a response to the increasing isolation of the Gaza Strip and the tightening of Israel’s closure of the territory after Hamas won the Palestinian parliamentary elections. 1 A group of international activists concerned with the situation in the Gaza Strip were brainstorming ways to impact the situation when one suggested sailing a boat to Gaza. Although many of the activists thought it was a ludicrous idea at first, the idea gradually evolved into the Free Gaza movement and led to the launch of the first boats in the summer of 2008. At the time of writing in November 2010, the Free Gaza movement had organized nine missions, the ninth being the Freedom Flotilla sent in May 2010 with the Mavi Marmara as the flagship. The first five missions, sent between August 2008 and December 2008, reached the Gaza Strip successfully. On December 29, 2008, two days after the start of Operation Cast Lead, Free Gaza sent an emergency boat, carrying doctors, journalists, and medical supplies. An Israeli warship repeatedly rammed the small Free Gaza boat in international waters, causing severe damage to the vessel, and the mission had to be aborted. Two weeks later the Israeli navy intercepted another attempt by Free Gaza to reach Gaza, still during Operation Cast Lead. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".