“You Never Told Me”: The Pedagogical Content Knowledge (PCK) of Israel Education
Bibliographic record
Abstract
Abstract Although there have been many studies describing the practice of Israel education, few, if any, have explored the pedagogical content knowledge (PCK) of this subject matter—what teachers know about how best to teach it. In this phenomenological study, 20 teachers in English-speaking Jewish high school settings in the USA, Israel, Australia, and Canada were interviewed to describe the components of their PCK. This research demonstrates that disappointment in the idealization of Israel by previous generations has impacted how today’s Israel educators in Jewish high schools understand the purposes of their discipline, the curricular choices they make, the instructional strategies they employ, and the context in which they teach. Addressing this unique phenomenon, which has come to be known by the slogan “you never told me,” has become a guiding instructional principle in the field as teachers about Israel prepare their students to maintain their Jewish commitments while transitioning from an immersive Jewish learning environment to becoming nuanced participants in conversations concerning Israel on college and university campuses. In addition to contributing to limited discourse on the teacher knowledge of Israel educators to improve the practice of the field, the findings of this paper emphasize the need for a pedagogy for complex Israel education deepening nuance and commitment to Israel. On the basis of the findings, we propose a model with eight design principles for how to do Israel education effectively in Jewish education frameworks, both formal and informal.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".