Bibliographic record
Abstract
Igrew up in Montreal where family, day school, youth groups, summer camp, and Jewish Studies at McGill University were all interwoven threads of a Jewish life that circled around home. expected trajectory: do well in school, get married, create a Jewish home. Never thinking of myself as different, I fully expected to be part of that continuum. But mentors and circumstances intervened: Professor Ben Ravid, a Jewish History professor, pushed me to apply to the Hornstein Program at Brandeis University. There, Professor Bernard Reisman became a mentor and encouraged me to see that professional opportunities were limitless. Professor Ruth Wisse and Nahum Ravel modeled behavior of Jewish leadership and learning that I wanted to emulate. These mentors convinced me that no opportunities in the Jewish community would be barred to me because I was a woman, and they helped me strategize my future. Soon I moved into a male-dominated Jewish organizational culture where my predecessors had all been men and the top lay leaders determining my destiny were mostly men, as were my professional colleagues. At that time, women moving up the general corporate ladder dressed in manly suits, wore ties, and learned all the necessary skills and metaphors to make it in that world. The mantra became, Do anything possible to avoid accentuating a femininity that leads to being stereotyped. While I shared the same intensity and commitment to the job as my male colleagues and friends, I also brought to the workplace characteristics often associated with the way women lead — characteristics that had been previously undervalued or ridiculed, and that I hoped to turn into an honest advantage. This became obvious when I was appointed Executive of the Jewish Federation of I brought to the workplace characteristics often associated with the way women lead. Greater Hartford in 1992.1 believe I offered a needed compassion toward a community that had suffered severe economic setbacks, an annual campaign in free-fall, and despondent lay leaders and professionals. For example, instead of abandoning major donors who were unable to continue making highlevel gifts, I suggested we create a strategy that honored them for past generosity and worked with their As a result of maintaining these relationships, when their economic situations improved, they remembered and returned as donors. Initially I was criticized for being too nurturing — not businesslike. But the strategy worked. leadership style reflects the principles of other women leaders. I create strategies for the communities I serve by orchestrating relationships and creating inclusive rather than hierarchical environments. Shaping such work settings is labor intensive and can leave room for surprises. It also occasions the risk of appearing weak. There have been times when I wondered if my leadership was being scrutinized more closely because I am a woman. What do I need to do to ensure the community never regrets its unusual decision to hire a woman for my position? I share these doubts with many women executives in the corporate world. In addition to being a new executive, I was a new mother. Family came before my job — a statement I made public to both the search committee and the Federation Board. As concerned as I was about doing a good job, I was equally aware of the voices that said, My career is only part of my identity. Do right for your children. Above all, be a good parent. Being candid about these issues helped create a family-sensitive work environment that accepted as the norm taking a child to the doctor and leav-
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.043 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".