Bibliographic record
Abstract
In 2012 I made two trips into Gaza City to work with staff of the American International School, Gaza. My employing company at the time had a contract with USAID to support the school in its development and progress toward accreditation. My role was to undertake an analysis of the school academic program and develop an improvement plan with the leadership group and board, incorporating pedagogical training for teaching staff that had extremely limited opportunities to external support and expertise due to isolation from the outside world. What I experienced in my visits to Gaza had a profound effect on me as a person and as an educator as I witnessed the passion and commitment of teachers working with students in the most trying of circumstances. The first three days of my initial visit coincided with a serious escalation of hostilities, and observing the school in operation throughout this time was inspiring and terrifying at the same time. My paper will recount my experience working alongside colleagues in challenging facilities, with minimal resources, amid the constant threat of danger. It will tell the story of how teacher commitment and dedication, matched by a strong student desire to learn can create truly inspiring learning communities. I will focus partly on the pedagogical training to assist teachers in sustaining an engaging learning environment in this environment, but also on the centrality of the school culture in a hostile environment where hope and aspiration emerge, and bring hope for the future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".